Neuroimaging studies examining the effects of aging and neuropsychiatric disorders on the cerebral cortex have largely been based on measures of cortical volume. Given that cortical volume is a product of thickness and surface area, it is plausible that measures of volume capture at least 2 distinct sets of genetic influences. The present study aims to examine the genetic relationships between measures of cortical surface area and thickness. Participants were men in the Vietnam Era Twin Study of Aging (110 monozygotic pairs and 92 dizygotic pairs). Mean age was 55.8 years (range: 51–59). Bivariate twin analyses were utilized in order to estimate the heritability of cortical surface area and thickness, as well as their degree of genetic overlap. Total cortical surface area and average cortical thickness were both highly heritable (0.89 and 0.81, respectively) but were essentially unrelated genetically (genetic correlation = 0.08). This pattern was similar at the lobar and regional levels of analysis. These results demonstrate that cortical volume measures combine at least 2 distinct sources of genetic influences. We conclude that using volume in a genetically informative study, or as an endophenotype for a disorder, may confound the underlying genetic architecture of brain structure.

Significance: Here we show that developmental and adult aging-related changes in cortical thickness follow closely the genetic organization of the cerebral cortex. A total of 1,633 MRI scans from 974 participants from 4.1 to 88.5 y of age were used to measure longitudinal changes in cortical thickness, and the topographic pattern of change was compared with the genetic relationship between cortical subdivisions of maximal shared genetic influence, obtained from an independent sample of 406 middle-aged twins. Cortical changes due to maturation and adult age changes adhered to the genetic organization of the cortex, indicating that individual differences in cortical architecture in middle-aged adults have a neurodevelopmental origin and that genetic factors affect cortical changes through life.

Microarray techniques hold great promise for identifying risk factors for schizophrenia (SZ) but have not yet generated widely reproducible results due to methodological differences between studies and the high risk of type I inferential errors. Here we established a protocol for conservative analysis and interpretation of gene expression data from the dorsolateral prefrontal cortex of SZ patients using statistical and bioinformatic methods that limit false positives. We also compared brain gene expression profiles with those from peripheral blood cells of a separate sample of SZ patients to identify disease-associated genes that generalize across tissues and populations and further substantiate the use of gene expression profiling of blood for detecting valid SZ biomarkers. Implementing this systematic approach, we: ( i ) discovered 177 putative SZ risk genes in brain, 28 of which map to linked chromosomal loci; ( ii ) delineated six biological processes and 12 molecular functions that may be particularly disrupted in the illness; ( iii ) identified 123 putative SZ biomarkers in blood, 6 of which ( BTG1 , GSK3A , HLA-DRB1 , HNRPA3 , SELENBP1 , and SFRS1 ) had corresponding differential expression in brain; ( iv ) verified the differential expression of the strongest candidate SZ biomarker ( SELENBP1 ) in blood; and ( v ) demonstrated neuronal and glial expression of SELENBP1 protein in brain. The continued application of this approach in other brain regions and populations should facilitate the discovery of highly reliable and reproducible candidate risk genes and biomarkers for SZ. The identification of valid peripheral biomarkers for SZ may ultimately facilitate early identification, intervention, and prevention efforts as well.

Building the Brain: Brain connectivity is often described as a network of discrete independent cables analogous to a switchboard, but how is the physical structure of the brain constructed (see the Perspective by Zilles and Amunts )? Wedeen et al. (p. 1628 ) used high-resolution diffusion tensor imaging in humans and four species of nonhuman primates to identify and compare the geometric structure of large fiber tracts in the brain. Fiber tracts followed a highly constrained and regular geometry, which may provide an efficient solution for pathfinding during ontogenetic development. Much of development occurs through elaboration and assembly of semiautonomous building blocks. Chen et al. (p. 1634 ) applied statistical analysis to the form of the human cortex in brain-imaging studies that compared more than 400 di- and mono-zygotic twins. The findings suggest that the structure of the human cortex is defined by genetics.

We reviewed potential neuropsychological risk indicators for schizophrenia by addressing two broad questions about neuropsychological performance in biological relatives of schizophrenia patients: (1) Is there evidence of deficits, and, if so, (2) are those deficits similar to deficits found in schizophrenia patients themselves? There has not yet been adequate validation of most neuropsychological risk indicators, but promising leads have emerged from studies of relatives of persons with schizophrenia. The strongest evidence of impairment in relatives was in sustained attention, perceptual-motor speed, and concept formation and abstraction; to a slightly lesser extent, mental control/encoding (primarily with distraction) was implicated as well. Impairments in verbal memory and verbal fluency were also found, although these have been less well studied. The pattern of deficits paralleled that found in schizophrenia patients, thus suggesting dysfunction in prefrontal, temporal-limbic, and attentional systems. Findings were similar for children and adult relatives of schizophrenia patients. It is suggested that future studies (1) emphasize comprehensive test batteries, (2) develop composite neuropsychological measures, (3) use profile and deviant-responder analyses, (4) include psychiatric comparison groups, and (5) integrate neuropsychological assessments with brain imaging techniques.