Genetic effects on the cerebellar role in working memory: Same brain, different genes?

Over the past several years, evidence has accumulated showing that the cerebellum plays a significant role in cognitive function. Here we show, in a large genetically informative twin sample (n= 430; aged 16-30. years), that the cerebellum is strongly, and reliably (n=30 rescans), activated during an n-back working memory task, particularly lobules I-IV, VIIa Crus I and II, IX and the vermis. Monozygotic twin correlations for cerebellar activation were generally much larger than dizygotic twin correlations, consistent with genetic influences. Structural equation models showed that up to 65% of the variance in cerebellar activation during working memory is genetic (averaging 34% across significant voxels), most prominently in the lobules VI, and VIIa Crus I, with the remaining variance explained by unique/unshared environmental factors. Heritability estimates for brain activation in the cerebellum agree with those found for working memory activation in the cerebral cortex, even though cerebellar cyto-architecture differs substantially. Phenotypic correlations between BOLD percent signal change in cerebrum and cerebellum were low, and bivariate modeling indicated that genetic influences on the cerebellum are at least partly specific to the cerebellum. Activation on the voxel-level correlated very weakly with cerebellar gray matter volume, suggesting specific genetic influences on the BOLD signal. Heritable signals identified here should facilitate discovery of genetic polymorphisms influencing cerebellar function through genome-wide association studies, to elucidate the genetic liability to brain disorders affecting the cerebellum.

Extending genetic linkage analysis to diffusion tensor images to map single gene effects on brain fiber architecture

We extended genetic linkage analysis - an analysis widely used in quantitative genetics - to 3D images to analyze single gene effects on brain fiber architecture. We collected 4 Tesla diffusion tensor images (DTI) and genotype data from 258 healthy adult twins and their non-twin siblings. After high-dimensional fluid registration, at each voxel we estimated the genetic linkage between the single nucleotide polymorphism (SNP), Val66Met (dbSNP number rs6265), of the BDNF gene (brain-derived neurotrophic factor) with fractional anisotropy (FA) derived from each subject's DTI scan, by fitting structural equation models (SEM) from quantitative genetics. We also examined how image filtering affects the effect sizes for genetic linkage by examining how the overall significance of voxelwise effects varied with respect to full width at half maximum (FWHM) of the Gaussian smoothing applied to the FA images. Raw FA maps with no smoothing yielded the greatest sensitivity to detect gene effects, when corrected for multiple comparisons using the false discovery rate (FDR) procedure. The BDNF polymorphism significantly contributed to the variation in FA in the posterior cingulate gyrus, where it accounted for around 90-95% of the total variance in FA. Our study generated the first maps to visualize the effect of the BDNF gene on brain fiber integrity, suggesting that common genetic variants may strongly determine white matter integrity.

Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study

We developed an analysis pipeline enabling population studies of HARDI data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. We applied tensor-driven 3D fluid registration to HARDI, resampling the spherical fiber orientation distribution functions (ODFs) in appropriate Riemannian manifolds, after ODF regularization and sharpening. Fitting structural equation models (SEM) from quantitative genetics, we evaluated genetic influences on the Jensen-Shannon divergence (JSD), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (GFA) a measure of fiber integrity. With random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects' intelligence quotient (IQ). Fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. Our methods show promise for discovering genes affecting fiber connectivity in the brain.

Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

Genetics of brain fiber architecture and intellectual performance

The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal (a 2 = 0.55, p = 0.04, left; a 2 = 0.74, p = 0.006, right), bilateral parietal (a 2 = 0.85, p < 0.001, left; a 2 = 0.84, p < 0.001, right), and left occipital (a 2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto- occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata (p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition.

Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29

White matter microstructure is under strong genetic control, yet it is largely unknown how genetic influences change from childhood into adulthood. In one of the largest brain mapping studies ever performed, we determined whether the genetic control over white matter architecture depends on age, sex, socioeconomic status (SES), and intelligence quotient (IQ). We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4-Tesla), in 705 twins and their siblings (age range 12-29; 290. M/415. F). White matter integrity was quantified using a widely accepted measure, fractional anisotropy (FA). We fitted gene-environment interaction models pointwise, to visualize brain regions where age, sex, SES and IQ modulate heritability of fiber integrity. We hypothesized that environmental factors would start to outweigh genetic factors during late childhood and adolescence. Genetic influences were greater in adolescence versus adulthood, and greater in males than in females. Socioeconomic status significantly interacted with genes that affect fiber integrity: heritability was higher in those with higher SES. In people with above-average IQ, genetic factors explained over 80% of the observed FA variability in the thalamus, genu, posterior internal capsule, and superior corona radiata. In those with below-average IQ, however, only around 40% FA variability in the same regions was attributable to genetic factors. Genes affect fiber integrity, but their effects vary with age, sex, SES and IQ. Gene-environment interactions are vital to consider in the search for specific genetic polymorphisms that affect brain integrity and connectivity.

The multivariate A/C/E model and the genetics of fiber architecture

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 monozygotic (MZ) twins and 25 dizygotic (DZ) twin pairs (100 subjects total). First, the structural and DT scans were linearly co-registered. Structural MR scans were nonlinearly mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was measured between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quanititative genetic studies can take advantage of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.

Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture

Recent advances in diffusion-weighted MRI (DWI) have enabled studies of complex white matter tissue architecture in vivo. To date, the underlying influence of genetic and environmental factors in determining central nervous system connectivity has not been widely studied. In this work, we introduce new scalar connectivity measures based on a computationally-efficient fast-marching algorithm for quantitative tractography. We then calculate connectivity maps for a DTI dataset from 92 healthy adult twins and decompose the genetic and environmental contributions to the variance in these metrics using structural equation models. By combining these techniques, we generate the first maps to directly examine genetic and environmental contributions to brain connectivity in humans. Our approach is capable of extracting statistically significant measures of genetic and environmental contributions to neural connectivity.

Genetic architecture of subcortical brain regions: Common and region-specific genetic contributions

Understanding the aetiology of patterns of variation within and covariation across brain regions is key to advancing our understanding of the functional, anatomical and developmental networks of the brain. Here we applied multivariate twin modelling and principal component analysis (PCA) to investigate the genetic architecture of the size of seven subcortical regions (caudate nucleus, thalamus, putamen, pallidum, hippocampus, amygdala and nucleus accumbens) in a genetically informative sample of adolescents and young adults (N=1038; mean age=21.6±3.2years; including 148 monozygotic and 202 dizygotic twin pairs) from the Queensland Twin IMaging (QTIM) study. Our multivariate twin modelling identified a common genetic factor that accounts for all the heritability of intracranial volume (0.88) and a substantial proportion of the heritability of all subcortical structures, particularly those of the thalamus (0.71 out of 0.88), pallidum (0.52 out of 0.75) and putamen (0.43 out of 0.89). In addition, we also found substantial region-specific genetic contributions to the heritability of the hippocampus (0.39 out of 0.79), caudate nucleus (0.46 out of 0.78), amygdala (0.25 out of 0.45) and nucleus accumbens (0.28 out of 0.52). This provides further insight into the extent and organization of subcortical genetic architecture, which includes developmental and general growth pathways, as well as the functional specialization and maturation trajectories that influence each subcortical region. This multivariate twin study identifies a common genetic factor that accounts for all the heritability of intracranial volume (0.88) and a substantial proportion of the heritability of all subcortical structures, particularly those of the thalamus (0.71 out of 0.88), pallidum (0.52 out of 0.75) and putamen (0.43 out of 0.89). In parallel, it also describes substantial region-specific genetic contributions to the heritability of the hippocampus (0.39 out of 0.79), caudate nucleus (0.46 out of 0.78), amygdala (0.25 out of 0.45) and nucleus accumbens (0.28 out of 0.52).

Mapping hippocampal and ventricular change in Alzheimer disease

We developed an anatomical mapping technique to detect hippocampal and ventricular changes in Alzheimer disease (AD). The resulting maps are sensitive to longitudinal changes in brain structure as the disease progresses. An anatomical surface modeling approach was combined with surface-based statistics to visualize the region and rate of atrophy in serial MRI scans and isolate where these changes link with cognitive decline. Fifty-two high-resolution MRI scans were acquired from 12 AD patients (age: 68.4 ± 1.9 years) and 14 matched controls (age: 71.4 ± 0.9 years), each scanned twice (2.1 ± 0.4 years apart). 3D parametric mesh models of the hippocampus and temporal horns were created in sequential scans and averaged across subjects to identify systematic patterns of atrophy. As an index of radial atrophy, 3D distance fields were generated relating each anatomical surface point to a medial curve threading down the medial axis of each structure. Hippocampal atrophic rates and ventricular expansion were assessed statistically using surface-based permutation testing and were faster in AD than in controls. Using color-coded maps and video sequences, these changes were visualized as they progressed anatomically over time. Additional maps localized regions where atrophic changes linked with cognitive decline. Temporal horn expansion maps were more sensitive to AD progression than maps of hippocampal atrophy, but both maps correlated with clinical deterioration. These quantitative, dynamic visualizations of hippocampal atrophy and ventricular expansion rates in aging and AD may provide a promising measure to track AD progression in drug trials.

Corrigendum to "Mapping hippocampal and ventricular change in Alzheimer disease" [NeuroImage 22 (2004) 1754-1766] (DOI:10.1016/j.neuroimage.2004.03.040)

We recently noticed an error in the demographic data in this article. The validity of the findings and the conclusions of the paper is not affected. However, there is an error in the reported sample size and in the means and standard deviations of the subjects’ ages and MMSE scores. We would like to correct this error, which came to light when we were re-analyzing the data for a meta-analysis. The error occurred because an older version of a spreadsheet was incorrectly used when reporting the sample composition. Instead of examining 12 Alzheimer's disease patients and 14 healthy elderly controls, we in fact examined 17 Alzheimer’s disease patients and 14 healthy elderly controls. All maps and morphometric data reported in the paper are correct, except that the sample size was in fact slightly higher than that originally reported, and the maps computed in the paper were based on the larger sample (which included five more subjects in the Alzheimer’s disease group). All of the maps and figures in the paper are correct, and the conclusions of the paper are unchanged. We apologize for this error, which falls under the sole responsibility of the first author. The corrected demographic information appears below.

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.

MR image-based measurement of rates of change in volumes of brain structures. Part II: Application to a study of Alzheimer's disease and normal aging

We present global and regional rates of brain atrophy measured on serially acquired Tl-weighted brain MR images for a group of Alzheimer's disease (AD) patients and age-matched normal control (NC) subjects using the analysis procedure described in Part I. Three rates of brain atrophy: the rate of atrophy in the cerebrum, the rate of lateral ventricular enlargement and the rate of atrophy in the region of temporal lobes, were evaluated for 14 AD patients and 14 age-matched NC subjects. All three rates showed significant differences between the two groups. However, the greatest separation of the two groups was obtained when the regional rates were combined. This application has demonstrated that rates of brain atrophy, especially in specific regions of the brain, based on MR images can provide sensitive measures for evaluating the progression of AD. These measures will be useful for the evaluation of therapeutic effects of novel therapies for AD.

Middle managers' uncertainty management during organizational change

Purpose This study aims to identify factors that facilitate or inhibit middle managers' experience of uncertainty management during organizational change. Design/methodology/approach The approach is qualitative and involved interviews with 40 middle managers from a range of organizations. Findings Analysis revealed that at the pre‐implementation stage, uncertainty focused on the strategic concept of the change, whereas at implementation, uncertainty related to the appropriate procedures to implement. Middle managers’ uncertainty management was found to be important in assisting their employees in the change transition. The factors identified as being either facilitators or barriers to uncertainty management focused on themes related to the design of change, communication with both senior management and their own staff, support from senior management, role conflict, and peer interaction. A model was created to link facilitators and barriers with uncertainty to guide future research. Research limitations/implications Implications for organizational change research along with practical implications are discussed. Originality/value This study provides insight into the positive contributions middle managers can make during change, along with suggesting what factors are facilitators or barriers to this positive role.

A longitudinal study of employee adaptation to organizational change: The role of change-related information and change-related self-efficacy

This study examined the role of information, efficacy, and 3 stressors in predicting adjustment to organizational change. Participants were 589 government employees undergoing an 18-month process of regionalization. To examine if the predictor variables had long-term effects on adjustment, the authors assessed psychological well-being, client engagement, and job satisfaction again at a 2-year follow-up. At Time 1, there was evidence to suggest that information was indirectly related to psychological well-being, client engagement, and job satisfaction, via its positive relationship to efficacy. There also was evidence to suggest that efficacy was related to reduced stress appraisals, thereby heightening client engagement. Last, there was consistent support for the stress-buffering role of Time 1 self-efficacy in the prediction of Time 2 job satisfaction.