MAPPING GENETIC INFLUENCES ON HUMAN BRAIN STRUCTURE 1 Paul Thompson PhD, 2 Tyrone D. Cannon PhD, 1 Arthur W. Toga PhD 1 Laboratory of Neuro Imaging and Brain Mapping Division, Department of Neurology, UCLA School of Medicine 2 Departments of Psychology, Psychiatry, and Human Genetics, UCLA School of Medicine An Invited Paper for: Annals of Medicine Review Article for the Series on Trends in Molecular Medicine Running Title (50 chars. max.): Mapping Genetic Influences on Brain Structure Revised: June 4, 2002 Please address correspondence to: Dr. Paul Thompson (Room 4238, Reed Neurological Research Center) Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine 710 Westwood Plaza, Los Angeles, CA 90095-1769, USA Phone: (310) 206-2101 Fax: (310) 206-5518 E-mail: [email protected]
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MAPPING GENETIC INFLUENCES
ON HUMAN BRAIN STRUCTURE
1Paul Thompson PhD, 2Tyrone D. Cannon PhD, 1Arthur W. Toga PhD
1Laboratory of Neuro Imaging and Brain Mapping Division, Department of Neurology, UCLA School of Medicine
2Departments of Psychology, Psychiatry, and Human Genetics, UCLA School of Medicine
An Invited Paper for:
Annals of Medicine
Review Article for the Series on
Trends in Molecular Medicine
Running Title (50 chars. max.): Mapping Genetic Influences on Brain Structure Revised: June 4, 2002
Please address correspondence to:
Dr. Paul Thompson (Room 4238, Reed Neurological Research Center)
Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine 710 Westwood Plaza, Los Angeles, CA 90095-1769, USA
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Figure Legends
Fig. 1. Creating 3D Average Brain Templates for a Population. Before computing individual anatomical differences, it is useful to create an average
model of anatomy for a specific population. If MRI scans are mutually aligned and their intensities are averaged together pixel-by-pixel [(a); Evans et
al., 1994], cortical features are washed away. To retain these features in the group average [(b),(c)], a procedure called cortical pattern matching can be
used (see Thompson et al., 2000 for details). From each individual’s MRI scan (d) a cortical model [(e),(f)] consisting of discrete triangular elements
(g) is created and flattened (panel 1), along with digital models of cortical sulci traced on the brain surface. A warping field drives the flat map (1), and
a color code indexing corresponding 3D cortical positions (3),(4), to match an average set of flat 2D sulcal curves (2). If these color images are
averaged across subjects and decoded before cortical pattern matching (3), a smooth average cortex (5) is produced. If they are warped first (5),
averaged, and decoded, a crisp average cortex appears in which anatomical features are reinforced and appear in their mean stereotaxic locations (6).
Such cortical averages provide a standard template relative to which individual differences may be measured (Fig. 2). Using warping (4), cortical data
can be transferred, from individuals whose anatomy is different, onto a common anatomic template for comparison and integration.
Fig. 2. Measuring Individual Brain Differences and Population Variability. When a individual brain (brown mesh, (a)) is globally aligned and scaled
to match a group average cortical model (white surface), a 3D deformation is computed to match its gyral anatomy with the group average (pink
colors: large deformations, (b)). The 3D root mean square magnitude of these deformation vectors (variability map, (c)) shows that gyral pattern
variability is greatest in perisylvian language areas (red colors). 3D confidence regions for gyral variations can be also stored locally to detect cortical
abnormalities ((d), Thompson et al., 1997). Ellipsoids, (d), are elongated along directions in which normal variation is greatest; pink colors denote
greatest anatomic variation. Deformations that match the gyral anatomy of one hemisphere with a reflected version of the opposite hemisphere can be
averaged across subjects to detect anatomic asymmetries. These are greatest in perisylvian cortices (red colors, (e),(f); Thompson et al., 2001;
Geschwind and Levitsky, 1968, first observed this feature in a volumetric study). Anatomic asymmetry is under greater genetic control in right-
handers, suggesting a loss of a genetically programmed ‘right-shift’ phenotype in left-handers (Geschwind et al., 2002). All these maps provide
detailed structural phenotypes that can be mined for genetic influences (Fig. 4). The maps shown here are based on a group of 20 healthy elderly
subjects, but can be recomputed for any population.
Fig. 3. Mapping Gray Matter Deficits in a Population. Measures of gray matter (a) can be computed from MRI scans and compared across individuals
and groups. Data from corresponding cortical regions are compared using cortical pattern matching (Fig. 1). Patients with mild to moderate
Alzheimer’s disease show a severe loss of gray matter [(b),(c)] relative to matched healthy controls, especially in temporal cortices (where deficits
approach 30% locally – red colors). Patients with childhood onset schizophrenia show a progressive loss of gray matter, especially in temporal and
superior frontal cortices [(d),(e)]. These structural measures are tightly correlated with worsening symptoms (Thompson et al., 2001, 2002), offering a
promising endophenotype (biological marker) for genetic studies. These biological markers are likely to be more directly influenced by genes coding
for structural proteins, regulatory elements, and signaling molecules, than clinical symptoms, such as hallucinations or disordered thinking.
Fig. 4. Mapping Genetic Influences on Brain Structure: Heritability Maps. Color-coded maps (left columns) show local gray matter correlations
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between MZ and DZ twins. Falconer’s heritability formula (Falconer, 1989) is applied to data from corresponding cortical regions (within and across
twin pairs). The resulting value of h2, and its significance (lower right panel) is plotted at each cortical point. Note the significant genetic control in an
anatomical band encompassing parietal, sensorimotor, and frontal cortices. Computationally, cortical points are indexed in spherical coordinates, as an
initially spherical surface mesh is deformed into the shape of each subject’s cortex (Fig. 1(d)), and the angular parameters are used subsequently for
computations. These mapping methods extend to other genetic designs, in which parameters denoting goodness of fit and coefficients describing
genetic and environmental effects could each be plotted on the cortex to reveal the spatial patterns of genetic influences.
Fig. 5. Risk Genes and Brain Structure. Typical MRI scans are shown from healthy elderly subjects with zero, one, and two ε4 alleles of ApoE gene,
which confers increased risk for late-onset Alzheimer’s disease (data courtesy of Gary Small, M.D., UCLA Center on Aging). The ε3 allele is more
prevalent, and considered normal. Patients at genetic risk may display metabolic and structural deficits before overt symptoms appear, suggesting that
genetic and imaging information may identify candidates for early treatment in dementia (Small et al., 2000). Note the hippocampal atrophy (H) and