Peter Kochunov

ALE meta-analysis: Controlling the false discovery rate and performing statistical contrasts

Activation likelihood estimation (ALE) has greatly advanced voxel‐based meta‐analysis research in the field of functional neuroimaging. We present two improvements to the ALE method. First, we evaluate the feasibility of two techniques for correcting for multiple comparisons: the single threshold test and a procedure that controls the false discovery rate (FDR). To test these techniques, foci from four different topics within the literature were analyzed: overt speech in stuttering subjects, the color‐word Stroop task, picture‐naming tasks, and painful stimulation. In addition, the performance of each thresholding method was tested on randomly generated foci. We found that the FDR method more effectively controls the rate of false positives in meta‐analyses of small or large numbers of foci. Second, we propose a technique for making statistical comparisons of ALE meta‐analyses and investigate its efficacy on different groups of foci divided by task or response type and random groups of similarly obtained foci. We then give an example of how comparisons of this sort may lead to advanced designs in future meta‐analytic research. Hum Brain Mapp 25:155–164, 2005.

Cortical Thickness or Grey Matter Volume? The Importance of Selecting the Phenotype for Imaging Genetics Studies

Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies.

Genetic control over the resting brain

The default-mode network, a coherent resting-state brain network, is thought to characterize basal neural activity. Aberrant default-mode connectivity has been reported in a host of neurological and psychiatric illnesses and in persons at genetic risk for such illnesses. Whereas the neurophysiologic mechanisms that regulate default-mode connectivity are unclear, there is growing evidence that genetic factors play a role. In this report, we estimate the importance of genetic effects on the default-mode network by examining covariation patterns in functional connectivity among 333 individuals from 29 randomly selected extended pedigrees. Heritability for default-mode functional connectivity was 0.424 ± 0.17 (P = 0.0046). Although neuroanatomic variation in this network was also heritable, the genetic factors that influence default-mode functional connectivity and gray-matter density seem to be distinct, suggesting that unique genes influence the structure and function of the network. In contrast, significant genetic correlations between regions within the network provide evidence that the same genetic factors contribute to variation in functional connectivity throughout the default mode. Specifically, the left parahippocampal region was genetically correlated with all other network regions. In addition, the posterior cingulate/precuneus region, medial prefrontal cortex, and right cerebellum seem to form a subnetwork. Default-mode functional connectivity is influenced by genetic factors that cannot be attributed to anatomic variation or a single region within the network. By establishing the heritability of default-mode functional connectivity, this experiment provides the obligatory evidence required before these measures can be considered as endophenotypes for psychiatric or neurological illnesses or to identify genes influencing intrinsic brain function.

Fractional anisotropy of water diffusion in cerebral white matter across the lifespan

Determining the time of peak of cerebral maturation is vital for our understanding of when cerebral maturation ceases and the cerebral degeneration in healthy aging begins. We carefully mapped changes in fractional anisotropy (FA) of water diffusion for eleven major cerebral white matter tracts in a large group (831) of healthy human subjects aged 11-90. FA is a neuroimaging index of micro-structural white matter integrity, sensitive to age-related changes in cerebral myelin levels, measured using diffusion tensor imaging. The average FA values of cerebral white matter (WM) reached peak at the age 32 ± 6 years. FA measurements for all but one major cortical white matter tract (cortico-spinal) reached peaks between 23 and 39 years of age. The maturation rates, prior to age-of-peak were significantly correlated (r=0.74; p=0.01) with the rates of decline, past age-of-peak. Regional analysis of corpus callosum (CC) showed that thinly-myelinated, densely packed fibers in the genu, that connect pre-frontal areas, maturated later and showed higher decline in aging than the more thickly myelinated motor and sensory areas in the body and splenium of CC. Our findings can be summarized as: associative, cerebral WM tracts that reach their peak FA values later in life also show progressively higher age-related decline than earlier maturing motor and sensory tracts. These findings carry multiple and diverse implications for both theoretical studies of the neurobiology of maturation and aging and for the clinical studies of neuropsychiatric disorders.

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group.

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).

Regional Spatial Normalization: Toward an Optimal Target

Purpose The purpose of this work was to develop methods for defining, constructing, and evaluating a “minimal deformation target” (MDT) brain for multisubject studies based on analysis of the entire group. The goal is to provide a procedure that will create a standard, reproducible target brain image based on common features of a group of three-dimensional MR brain images. Method The average deformation and dispersion distance, derived from discrete three-dimensional deformation fields (DFs), are used to identify the best individual target (BIT) brain. This brain is assumed to be the one with the minimal deformation bias within a group of MR brain images. The BIT brain is determined as the one with the minimal target quality score, our cost function based on the deformation displacement and dispersion distance. The BIT brain is then transformed to the MDT brain using an average DF to create an optimized target brain. This analysis requires the calculation of a large number of DFs. To overcome this limitation, we developed an analysis method (the fast method) that reduces the task from order N2 complexity to one of order N, a tremendous advantage for large-N studies. Results Multiscale correlation analysis in a group of 20 subjects demonstrated the superiority of warping using the MDT target brain, made from the BIT brain, over several individual and MDT-transformed target brains also from the group. Conclusion Analysis of three-dimensional DF provides a means to quickly create a reproducible MDT target brain for any set of subjects. Warping to the MDT target was shown by an independent multiscale correlation method to produce superior results.

An optimized individual target brain in the Talairach coordinate system.

Abstract The goal of regional spatial normalization is to remove anatomical differences between individual three-dimensional brain images by warping them to match features of a single target brain. Current target brains are either an average, suitable for low-resolution brain mapping studies, or a single brain. While a single high-resolution target brain is desirable to match anatomical detail, it can lead to bias in anatomical studies. An optimization method to reduce the individual anatomical bias of the ICBM high-resolution brain template (HRBT), a high-resolution MRI target brain image used in many laboratories, is presented. The HRBT was warped to all images in a group of 27 normal subjects. Displacement fields were averaged to calculate the “minimal deformation target” (MDT) transformation for optimization. The greatest anatomical changes in the HRBT, following optimization, were observed in the superior precentral and postcentral gyri on the right, the right inferior occipital, the right posterior temporal lobes, and the lateral ventricles. Compared with the original HRBT, the optimized HRBT showed better anatomical matching to the group of 27 brains. This was quantified by the improvements in spatial cross-correlation and between the group of brains and the optimized HRBT (P

High dimensional endophenotype ranking in the search for major depression risk genes

BACKGROUND Despite overwhelming evidence that major depression is highly heritable, recent studies have localized only a single depression-related locus reaching genome-wide significance and have yet to identify a causal gene. Focusing on family-based studies of quantitative intermediate phenotypes or endophenotypes, in tandem with studies of unrelated individuals using categorical diagnoses, should improve the likelihood of identifying major depression genes. However, there is currently no empirically derived statistically rigorous method for selecting optimal endophentypes for mental illnesses. Here, we describe the endophenotype ranking value, a new objective index of the genetic utility of endophenotypes for any heritable illness. METHODS Applying endophenotype ranking value analysis to a high-dimensional set of over 11,000 traits drawn from behavioral/neurocognitive, neuroanatomic, and transcriptomic phenotypic domains, we identified a set of objective endophenotypes for recurrent major depression in a sample of Mexican American individuals (n = 1122) from large randomly selected extended pedigrees. RESULTS Top-ranked endophenotypes included the Beck Depression Inventory, bilateral ventral diencephalon volume, and expression levels of the RNF123 transcript. To illustrate the utility of endophentypes in this context, each of these traits were utlized along with disease status in bivariate linkage analysis. A genome-wide significant quantitative trait locus was localized on chromsome 4p15 (logarithm of odds = 3.5) exhibiting pleiotropic effects on both the endophenotype (lymphocyte-derived expression levels of the RNF123 gene) and disease risk. CONCLUSIONS The wider use of quantitative endophenotypes, combined with unbiased methods for selecting among these measures, should spur new insights into the biological mechanisms that influence mental illnesses like major depression.

Genetics of microstructure of cerebral white matter using diffusion tensor imaging

We analyzed the degree of genetic control over intersubject variability in the microstructure of cerebral white matter (WM) using diffusion tensor imaging (DTI). We performed heritability, genetic correlation and quantitative trait loci (QTL) analyses for the whole-brain and 10 major cerebral WM tracts. Average measurements for fractional anisotropy (FA), radial (L( perpendicular)) and axial (L( vertical line)) diffusivities served as quantitative traits. These analyses were done in 467 healthy individuals (182 males/285 females; average age 47.9+/-13.5 years; age range: 19-85 years), recruited from randomly-ascertained pedigrees of extended families. Significant heritability was observed for FA (h(2)=0.52+/-0.11; p=10(-7)) and L( perpendicular) (h(2)=0.37+/-0.14; p=0.001), while L( vertical line) measurements were not significantly heritable (h(2)=0.09+/-0.12; p=0.20). Genetic correlation analysis indicated that the FA and L( perpendicular) shared 46% of the genetic variance. Tract-wise analysis revealed a regionally diverse pattern of genetic control, which was unrelated to ontogenic factors, such as tract-wise age-of-peak FA values and rates of age-related change in FA. QTL analysis indicated linkages for whole-brain average FA (LOD=2.36) at the marker D15S816 on chromosome 15q25, and for L( perpendicular) (LOD=2.24) near the marker D3S1754 on the chromosome 3q27. These sites have been reported to have significant co-inheritance with two psychiatric disorders (major depression and obsessive-compulsive disorder) in which patients show characteristic alterations in cerebral WM. Our findings suggest that the microstructure of cerebral white matter is under a strong genetic control and further studies in healthy as well as patients with brain-related illnesses are imperative to identify the genes that may influence cerebral white matter.

Column-Based Model of Electric Field Excitation of Cerebral Cortex

A model to explain the orientation selectivity of the neurophysiologic effects of electric‐field transients applied to cerebral cortex is proposed and supported with neuroimaging evidence. Although it is well known that transcranial magnetic stimulation (TMS) excites cerebral cortex in an orientation‐selective manner, a neurophysiologically compelling explanation of this phenomenon has been lacking. It is generally presumed that TMS‐induced excitation is mediated by horizontal fibers in the cortical surfaces nearest to the stimulating coil, i.e., at the gyral crowns. No evidence exists, however, that horizontal fibers are orientation selective either anatomically or physiologically. We used positron emission tomography to demonstrate that TMS‐induced cortical activation is selectively sulcal. This observation allows the well‐established columnar organization of cerebral cortex to be invoked to explain the observed orientation selectivity. In addition, Rushtons cosine principle can used to model stimulation efficacy for an electrical field applied at any cortical site at any intensity and in any orientation. Hum. Brain Mapp. 22:1–16, 2004.