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Overview of Neuroimaging Phenotypes

Imaging Genetics Course, OHBM Seattle, 2013
by

Anderson Winkler

on 8 July 2016

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Transcript of Overview of Neuroimaging Phenotypes

Overview of
Neuroimaging Phenotypes

Anderson M. Winkler
Surface-based
Volume-based
Definition
A
phenotype
consists of the observable biochemical, physiological or morphological characteristics of an individual determined by his genotype and environmental influences.
Winawer, 2006.
Establishing and using a phenotype
Data collection:
Complete and well planned.
Reliability:
Same results when repeated.
Validity:
Measurements reflect what they are supposed to measure.
Clinical and research objectives differ
Use of clinical data for research requires careful judgement:
Is the clinical data
complete
(e.g., all relevant MRI sequences collected)?
Is it
standardized
(last minute sequences or contrasts)?
Is it
quantitative
(radiological diagnoses vs. quantification)?
Quantitative vs. Qualitative
Quantitative
traits are more
powerful
and should be preferred, except when less reliable.

Replace a diagnosis for a
biomarker
tightly associated with the diagnosis whenever possible.
Schulze and McMahon 2004;
Bartlett and Frost, 2008.
Fundamental issues
Clinical heterogeneity:
multiple disorder subtypes.
Grouping or splitting syndromes:
stratification of phenotype is good if matches genotype.

Complicating factors:
Variable expressivity, penetrance;
Pleiotropy, locus heterogeneity;
GxG (epistasis) and GxE interactions.
Winawer, 2006.
Endophenotype
Associated with illness;
Heritable;
State-independent;
Co-segregate with illness.
Gottesman and Gould, 2003.
Patterns of inheritance
Mendelian:
single locus (autosomic/sex-linked, dominant/recessive).
Locus heterogeneity:
multiple loci, any of them causing the observable phenotype.
Pleiotropy:
single locus, multiple observable phenotypes.
Complex trait:
multiple loci and environmental factors affecting the phenotype, interactions between genes and phenotypes, often with mixed pleiotropy and genetic heterogeneity.
Other:
anticipation, mosaicism, uniparental disomy, genomic imprinting, mitochondrial inheritance.
Levels of expression
Endophenotypes
are
phenotypes.
Endophenotypes relate to
disorders
.
Endophenotypes are
not
invisible.
Terminology
Bearden et al., in press.
Overview of main imaging phenotypes
Structure:
Morphology:
thickness, area, volume, complexity, shape, tractography.
Tissue properties:
FA, MD, AD, RD, myelin content, GM/WM ratio, relaxometry, spectroscopy, susceptibility, magnetization transfer.
Function:
Activity:
task-based, task-free, connectivity (BOLD-fMRI, EEG, MEG, NIRS).
Physiology:
ASL, PET.
Phenomics
The systematic standardization of measures hypothesized to represent the complete phenotypic space for a given biological system, and their assessment in all members of a study population.
Freimer and Sabatti, 2003.
Congdon et al., 2010.
Cortical morphology
Winkler et al. 2010.
Distance between pia-mater and the gray-white interface.
Captures the variability of all 6 cortical layers combined and myelination.
Heritable, and controlled regionally by distinct genetic factors.
Rimol et al., 2010.
Area of the gray-matter interface, or of the pia-mater.
Regionalization/compartmentalization of the cortex:
Absolute values (e.g., in mm²)
Relative values (use of a reference brain)
For regions, depends on the definition of the borders
For vertexwise/facewise, depends on the features that drive the registration (morphology, function, myelination).
Like thickness, heritable and regionally influenced by distinct genetic factors.
Winkler et al., 2012;
Chen et al., 2012.
Measurable in voxel-based (e.g. VBM) and surface-based representations of the brain.
Mixture of thickness and area, in variable proportions across the cortical mantle, but reflects mostly surface area.
Ambiguous: susceptible to misregistration, misclassification, folding/gyrification, thickness, amount of GM, deformation fields.
Should be avoided.
Cortical thickness
Cortical area
Cortical volume
Eyler et al., 2012
Facewise
Winkler et al., 2010.
Chen et al., 2012.
Ratio of the pial surface and the convex hull. Can be computed for each vertex (LGI).
Gyrification index
Thompson et al., 1996.
Cortical organization, arealization.
Potential to drive the registration: good for areal analyses, and a completely
new way to do fMRI
.
Covariate/ancillary information for cortical thickness studies.
1/T1 imaging, T1/T2 ratio.
Myelin content
Ratio of the logs of the resolution scaling and the measurement scaling.
Cortical complexity
Fractal dimension
Indicator of a biological process different than cortical thickness.
Decreases with age and certain disorders, varies spatially, heritable.
May confound thickness measurements.
GM/WM contrast
Westlye et al., 2009.
Panizzon et al., 2012.
Subcortical structures
Can be the total volume or volume of GM or WM.
Use a global measurement of brain volume to account for brain size (brain volume, ICV).
Kremen et al., 2010.
Eyler et al, 2011.
Shape analysis
Mesh and/or parameterizable model the structures to analyse.
Multiple possibilities for analysis and interpretations depending on the method and the structure.
Styner et al., 2006.
Patenaude et al., 2011.
FSL/FIRST
Pievani et al., 2011.
Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD)
Relate to coherent direction of axons, the integrity of their walls, axonal transport systems, and myelin.
DTI, DKI, DSI, HARDI.
Diffusion-weighted imaging
Beaulieu, 2002.
Kochunov et al., 2011.
Tries to reconstruct fiber tracts using diffusion and anisotropy.
Do not reflect true tract locations, but likelihoods for tracts.
Results per subject.
Can be used to analyze termination points, to project anisotropy measurements (FA, MD, RD, AD), to define regions and for shape analyses.
Tractography
Jbabdi and Johansen-Berg, 2011;
Shi et al., 2013.
Brain structure: Other methods
Relaxometry:
Measurement of decay. Index of inflamation and perhaps other tissue changes.
Magnetization transfer:
Angiography. [Henkelman, 2001].
Susceptibility-weighted imaging:
sensitive to venous blood, hemorrhage and iron storage [Haacke et al., 2009; Mittal et al., 2009].
Spectroscopy:
Metabolites are heritable [Batouli et al, 2012].
Many of these may reflect mostly environmental influences, but potential use in phenomics/imaging genetics needs to be explored.
Brain activity and physiology
Smith et al., 2009.
Wager et al., 2013.
Task-free:
Task-based:
PET, ASL, EEG, MEG, NIRS.
Connectivity:
Other methods:
Rubinov and Sporns, 2010
(but see Zalesky et al., 2012).
Trait split into many pieces:
Voxels, vertices, faces:
best localizing power, noisier, multiple testing problem more severe.
Regions:
powerful if matches size of the effect, less noise, less localizing power.
Summarize data within region using average or first eigenvariate.
Use methods that preserve the variance.
Imaging-specific issues
Turnpenny and
Ellard, 2011.
Bilder et al, 2009.
Thanks!
Tom Nichols & J.-B. Poline

David Glahn
John Blangero
Peter Kochunov
Jack Kent Jr.
D. Reese McKay, Emma Sprooten, Emma Knowles
brainder.org
Wojczynski and Tiwari, 2008.
Avoid circularity
when defining regions. Use data from previous studies, or from an atlas, or from a database, e.g., BrainMap, NeuroSynth, Allen Brain.
Endophenotypic Ranking Value
Glahn et al., 2011.
(see also Toro et al, 2008)
Full transcript