Three-dimensional brain growth abnormalities in childhood-onset schizophrenia visualized by using tensor-based morphometry.

Earlier studies revealed progressive cortical gray matter (GM) loss in childhood-onset schizophrenia (COS) across both lateral and medial surfaces of the developing brain. Here, we use tensor-based morphometry to visualize white matter (WM) growth abnormalities in COS throughout the brain. Using high-dimensional elastic image registration, we compared 3D maps of local WM growth rates in COS patients and healthy children over a 5-year period, based on analyzing longitudinal brain MRIs from 12 COS patients and 12 healthy controls matched for age, gender, and scan interval. COS patients showed up to 2.2% slower growth rates per year than healthy controls in WM (P = 0.02, all P values corrected). The greatest differences were in the right hemisphere (P = 0.006). This asymmetry was attributable to a right slower than left hemisphere growth rate mapped in COS patients (P = 0.037) but not in healthy controls. WM growth rates reached 2.6% per year in healthy controls (P = 0.0002). COS patients showed only a 1.3% per year trend for growth in the left hemisphere (P = 0.066). In COS, WM growth rates were associated with improvement in the Children's Global Assessment Scale (R = 0.64, P = 0.029). Growth rates were reduced throughout the brain in COS, but this process appeared to progress in a front-to-back (frontal-parietal) fashion, and this effect was not attributable to lower IQ. Growth rates were correlated with functional prognosis and were visualized as detailed 3D maps. Finally, these findings also confirm that the progressive GM deficits seen in schizophrenia are not the result of WM overgrowth.

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.

How does angular resolution affect diffusion imaging measures?

A key question in diffusion imaging is how many diffusion-weighted images suffice to provide adequate signal-to-noise ratio (SNR) for studies of fiber integrity. Motion, physiological effects, and scan duration all affect the achievable SNR in real brain images, making theoretical studies and simulations only partially useful. We therefore scanned 50 healthy adults with 105-gradient high-angular resolution diffusion imaging (HARDI) at 4T. From gradient image subsets of varying size (6

Brain structure changes visualized in early- and late-onset blind subjects.

We examined 3D patterns of volume differences in the brain associated with blindness, in subjects grouped according to early and late onset. Using tensor-based morphometry, we mapped volume reductions and gains in 16 early-onset (EB) and 16 late-onset (LB) blind adults (onset <5 and >14 years old, respectively) relative to 16 matched sighted controls. Each subject's structural MRI was fluidly registered to a common template. Anatomical differences between groups were mapped based on statistical analysis of the resulting deformation fields revealing profound deficits in primary and secondary visual cortices for both blind groups. Regions outside the occipital lobe showed significant hypertrophy, suggesting widespread compensatory adaptations. EBs but not LBs showed deficits in the splenium and the isthmus. Gains in the non-occipital white matter were more widespread in the EBs. These differences may reflect regional alterations in late neurodevelopmental processes, such as myelination, that continue into adulthood.

Genetic influences on brain asymmetry: a DTI study of 374 twins and siblings.

Brain asymmetry, or the structural and functional specialization of each brain hemisphere, has fascinated neuroscientists for over a century. Even so, genetic and environmental factors that influence brain asymmetry are largely unknown. Diffusion tensor imaging (DTI) now allows asymmetry to be studied at a microscopic scale by examining differences in fiber characteristics across hemispheres rather than differences in structure shapes and volumes. Here we analyzed 4Tesla DTI scans from 374 healthy adults, including 60 monozygotic twin pairs, 45 same-sex dizygotic pairs, and 164 mixed-sex DZ twins and their siblings; mean age: 24.4years+/-1.9 SD). All DTI scans were nonlinearly aligned to a geometrically-symmetric, population-based image template. We computed voxel-wise maps of significant asymmetries (left/right differences) for common diffusion measures that reflect fiber integrity (fractional and geodesic anisotropy; FA, GA and mean diffusivity, MD). In quantitative genetic models computed from all same-sex twin pairs (N=210 subjects), genetic factors accounted for 33% of the variance in asymmetry for the inferior fronto-occipital fasciculus, 37% for the anterior thalamic radiation, and 20% for the forceps major and uncinate fasciculus (all L>R). Shared environmental factors accounted for around 15% of the variance in asymmetry for the cortico-spinal tract (R>L) and about 10% for the forceps minor (L>R). Sex differences in asymmetry (men>women) were significant, and were greatest in regions with prominent FA asymmetries. These maps identify heritable DTI-derived features, and may empower genome-wide searches for genetic polymorphisms that influence brain asymmetry.

3D mapping of brain differences in native signing congenitally and prelingually deaf subjects.

In the prelingual and congenital deaf, functional reorganization is known to occur throughout brain regions normally associated with hearing. However, the anatomical correlates of these changes are not yet well understood. Here, we perform the first tensor-based morphometric analysis of voxel-wise volumetric differences in native signing prelingual and congenitally deaf subjects when compared with hearing controls. We obtained T1-weighted scans for 14 native signing prelingual and congenitally deaf subjects and 16 age- and gender-matched controls. We used linear and fluid registration to align each image to a common template. Using the voxel-wise determinant of the Jacobian of the fluid deformation, significant volume increases, of up to 20%, were found in frontal lobe white matter regions including Broca's area, and adjacent regions involved in motor control and language production. A similar analysis was performed on hand-traced corpora callosa. A strong trend for group differences was found in the area of the splenium considered to carry fibers connecting the temporal (and occipital) lobes. These anatomical differences may reflect experience-mediated developmental differences in myelination and cortical maturation associated with prolonged monomodal sensory deprivation.

Alterations in midline cortical thickness and gyrification patterns mapped in children with 22q11.2 deletions.

The 22q11.2 deletion syndrome (velocardiofacial/DiGeorge syndrome) is a neurogenetic condition associated with visuospatial deficits, as well as elevated rates of attentional disturbance, mood disorder, and psychosis. Previously, we detected pronounced cortical thinning in superior parietal and right parieto-occipital cortices in patients with this syndrome, regions critical for visuospatial processing. Here we applied cortical pattern-matching algorithms to structural magnetic resonance images obtained from 21 children with confirmed 22q11.2 deletions (ages 8-17) and 13 demographically matched comparison subjects, in order to map cortical thickness across the medial hemispheric surfaces. In addition, cortical models were remeshed in frequency space to compute their surface complexity. Cortical maps revealed a pattern of localized thinning in the ventromedial occipital-temporal cortex, critical for visuospatial representation, and the anterior cingulate, a key area for attentional control. However, children with 22q11.2DS showed significantly increased gyral complexity bilaterally in occipital cortex. Regional gray matter volumes, particularly in medial frontal cortex, were strongly correlated with both verbal and nonverbal cognitive functions. These findings suggest that aberrant parieto-occipital brain development, as evidenced by both increased complexity and cortical thinning in these regions, may be a neural substrate for the deficits in visuospatial and numerical understanding characteristic of this syndrome.

Mapping the regional influence of genetics on brain structure variability--a tensor-based morphometry study.

Genetic and environmental factors influence brain structure and function profoundly. The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8+/-1.8 SD years). All 92 twins' 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject's anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions that have a more protracted maturational time-course.

Increased volume of the amygdala and hippocampus in bipolar patients treated with lithium.

Previous structural neuroimaging studies of bipolar disorder have reported conflicting findings in limbic structures. Medication heterogeneity of patient samples may have contributed to these inconsistencies. Using structural magnetic resonance imaging we assessed whether lithium treatment was associated with differences in amygdala and hippocampal volumes in a sample of bipolar adults. A total of 49 magnetic resonance imaging scans were collected from patients who were currently treated with or without lithium. Amygdala and hippocampal volumes were analyzed using tensor-based morphometry. Statistical between-group comparisons of deformation maps showed that patients treated with lithium exhibited significantly increased volumes of the amygdala and hippocampus compared with patients who were not taking lithium. Our findings may help to explain previous inconsistencies in the bipolar literature.

Neural phenotypes of common and rare genetic variants.

Neuroimaging methods offer a powerful way to bridge the gaps between genes, neurobiology and behavior. Such investigations may be further empowered by complementary strategies involving chromosomal abnormalities associated with particular neurobehavioral phenotypes, which can help to localize causative genes and better understand the genetics of complex traits in the general population. Here we review the evidence from studies using these convergent approaches to investigate genetic influences on brain structure: (1) studies of common genetic variations associated with particular neuroanatomic phenotypes, and (2) studies of possible 'genetic subtypes' of neuropsychiatric disorders with very high penetrance, with a focus on neuroimaging studies using novel computational brain mapping algorithms. Finally, we discuss the contribution of behavioral neurogenetics research to our understanding of the genetic basis of neuropsychiatric disorders in the broader population.

Greater cortical gray matter density in lithium-treated patients with bipolar disorder.

BACKGROUND: The neurobiological underpinnings of bipolar disorder are not well understood. Previous neuroimaging findings have been inconsistent; however, new methods for three-dimensional (3-D) computational image analysis may better characterize neuroanatomic changes than standard volumetric measures. METHODS: We used high-resolution magnetic resonance imaging and cortical pattern matching methods to map gray matter differences in 28 adults with bipolar disorder, 70% of whom were lithium-treated (mean age = 36.1 +/- 10.5; 13 female subject), and 28 healthy control subjects (mean age = 35.9 +/- 8.5; 11 female subjects). Detailed spatial analyses of gray matter density (GMD) were conducted by measuring local proportions of gray matter at thousands of homologous cortical locations. RESULTS: Gray matter density was significantly greater in bipolar patients relative to control subjects in diffuse cortical regions. Greatest differences were found in bilateral cingulate and paralimbic cortices, brain regions critical for attentional, motivational, and emotional modulation. Secondary region of interest (ROI) analyses indicated significantly greater GMD in the right anterior cingulate among lithium-treated bipolar patients (n = 20) relative to those not taking lithium (n = 8). CONCLUSIONS: These brain maps are consistent with previous voxel-based morphometry reports of greater GMD in portions of the anterior limbic network in bipolar patients and suggest neurotrophic effects of lithium as a possible etiology of these neuroanatomic differences.

Automated extraction of the cortical sulci based on a supervised learning approach.

It is important to detect and extract the major cortical sulci from brain images, but manually annotating these sulci is a time-consuming task and requires the labeler to follow complex protocols. This paper proposes a learning-based algorithm for automated extraction of the major cortical sulci from magnetic resonance imaging (MRI) volumes and cortical surfaces. Unlike alternative methods for detecting the major cortical sulci, which use a small number of predefined rules based on properties of the cortical surface such as the mean curvature, our approach learns a discriminative model using the probabilistic boosting tree algorithm (PBT). PBT is a supervised learning approach which selects and combines hundreds of features at different scales, such as curvatures, gradients and shape index. Our method can be applied to either MRI volumes or cortical surfaces. It first outputs a probability map which indicates how likely each voxel lies on a major sulcal curve. Next, it applies dynamic programming to extract the best curve based on the probability map and a shape prior. The algorithm has almost no parameters to tune for extracting different major sulci. It is very fast (it runs in under 1 min per sulcus including the time to compute the discriminative models) due to efficient implementation of the features (e.g., using the integral volume to rapidly compute the responses of 3-D Haar filters). Because the algorithm can be applied to MRI volumes directly, there is no need to perform preprocessing such as tissue segmentation or mapping to a canonical space. The learning aspect of our approach makes the system very flexible and general. For illustration, we use volumes of the right hemisphere with several major cortical sulci manually labeled. The algorithm is tested on two groups of data, including some brains from patients with Williams Syndrome, and the results are very encouraging.

Statistical properties of Jacobian maps and the realization of unbiased large-deformation nonlinear image registration.

Maps of local tissue compression or expansion are often computed by comparing magnetic resonance imaging (MRI) scans using nonlinear image registration. The resulting changes are commonly analyzed using tensor-based morphometry to make inferences about anatomical differences, often based on the Jacobian map, which estimates local tissue gain or loss. Here, we provide rigorous mathematical analyses of the Jacobian maps, and use themto motivate a new numerical method to construct unbiased nonlinear image registration. First, we argue that logarithmic transformation is crucial for analyzing Jacobian values representing morphometric differences. We then examine the statistical distributions of log-Jacobian maps by defining the Kullback-Leibler (KL) distance on material density functions arising in continuum-mechanical models. With this framework, unbiased image registration can be constructed by quantifying the symmetric KL-distance between the identity map and the resulting deformation. Implementation details, addressing the proposed unbiased registration as well as the minimization of symmetric image matching functionals, are then discussed and shown to be applicable to other registration methods, such as inverse consistent registration. In the results section, we test the proposed framework, as well as present an illustrative application mapping detailed 3-D brain changes in sequential magnetic resonance imaging scans of a patient diagnosed with semantic dementia. Using permutation tests, we show that the symmetrization of image registration statistically reduces skewness in the log-Jacobian map.

Abnormal cortical complexity and thickness profiles mapped in Williams syndrome.

We identified and mapped an anatomically localized failure of cortical maturation in Williams syndrome (WS), a genetic condition associated with deletion of approximately 20 contiguous genes on chromosome 7. Detailed three-dimensional (3D) maps of cortical thickness, based on magnetic resonance imaging (MRI) scans of 164 brain hemispheres, identified a delimited zone of right hemisphere perisylvian cortex that was thicker in WS than in matched controls, despite pervasive gray and white matter deficits and reduced total cerebral volumes. 3D cortical surface models were extracted from 82 T1-weighted brain MRI scans (256 x 192 x 124 volumes) of 42 subjects with genetically confirmed WS (mean +/- SD, 29.2 +/- 9.0 years of age; 19 males, 23 females) and 40 age-matched healthy controls (27.5 +/- 7.4 years of age; 16 males, 24 females). A cortical pattern-matching technique used 72 sulcal landmarks traced on each brain as anchors to align cortical thickness maps across subjects, build group average maps, and identify regions with altered cortical thickness in WS. Cortical models were remeshed in frequency space to compute their fractal dimension (surface complexity) for each hemisphere and lobe. Surface complexity was significantly increased in WS (p < 0.0015 and p < 0.0014 for left and right hemispheres, respectively) and correlated with temporoparietal gyrification differences, classified via Steinmetz criteria. In WS, cortical thickness was increased by 5-10% in a circumscribed right hemisphere perisylvian and inferior temporal zone (p < 0.002). Spatially extended cortical regions were identified with increased complexity and thickness; cortical thickness and complexity were also positively correlated in controls (p < 0.03). These findings visualize cortical zones with altered anatomy in WS, which merit additional study with techniques to assess function and connectivity.

Developmental changes in multivariate neuroanatomical patterns that predict risk for psychosis in 22q11.2 deletion syndrome.

The primary objective of the current prospective study was to examine developmental patterns of voxel-by-voxel gray and white matter volumes (GMV, WMV, respectively) that would predict psychosis in adolescents with 22q11.2 deletion syndrome (22q11.2DS), the most common known genetic risk factor for schizophrenia. We performed a longitudinal voxel-based morphometry analysis using structural T1 MRI scans from 19 individuals with 22q11.2DS and 18 typically developing individuals. In 22q11.2DS, univariate analysis showed that greater reduction in left dorsal prefrontal cortical (dPFC) GMV over time predicted greater psychotic symptoms at Time2. This dPFC region also showed significantly reduced volumes in 22q11.2DS compared to typically developing individuals at Time1 and 2, greater reduction over time in 22q11.2DS COMT(Met) compared to COMT(Val), and greater reduction in those with greater decline in verbal IQ over time. Leave-one-out Multivariate pattern analysis results (MVPA) on the other hand, showed that patterns of GM and WM morphometric changes over time in regions including but not limited to the dPFC predicted risk for psychotic symptoms (94.7-100% accuracy) significantly better than using univariate analysis (63.1%). Additional predictive brain regions included medial PFC and dorsal cingulum. This longitudinal prospective study shows novel evidence of morphometric spatial patterns predicting the development of psychotic symptoms in 22q11.2DS, and further elucidates the abnormal maturational processes in 22q11.2DS. The use of neuroimaging using MVPA may hold promise to predict outcome in a variety of neuropsychiatric disorders.

A nonconservative Lagrangian framework for statistical fluid registration-SAFIRA.

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented , where the deformation was regularized by penalizing deviations from a zero rate of strain. In , the terms regularizing the deformation included the covariance of the deformation matrices (Σ) and the vector fields (q) . Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

The contribution of genes to cortical thickness and volume.

We analyzed brain MRI data from 372 young adult twins to identify cortical regions in which gray matter thickness and volume are influenced by genetics. This was achieved using an A/C/E structural equation model that divides the variance of these traits, at each point on the cortex, into additive genetic (A), shared (C), and unique environmental (E) components. A strong genetic influence was found in frontal and parietal regions. In addition, we correlated cortical thickness with full-scale intelligence quotient for comparison with the A/C/E maps, and several regions where cortical structure was correlated with intelligence quotient are under genetic control. These cortical measures may be useful phenotypes to narrow the search for quantitative trait loci influencing brain structure.

A GENETIC ANALYSIS OF CORTICAL THICKNESS IN 372 TWINS.

Imaging genetics is a new field of neuroscience that blends methods from computational anatomy and quantitative genetics to identify genetic influences on brain structure and function. Here we analyzed brain MRI data from 372 young adult twins to identify cortical regions in which gray matter volume is influenced by genetic differences across subjects. Thickness maps, reconstructed from surface models of the cortical gray/white and gray/CSF interfaces, were smoothed with a 25 mm FWHM kernel and automatically parcellated into 34 regions of interest per hemisphere. In structural equation models fitted to volume values at each surface vertex, we computed components of variance due to additive genetic (A), shared (C) and unique (E) environmental factors, and tested their significance. Cortical regions in the vicinity of the perisylvian language cortex, and at the frontal and temporal poles, showed significant additive genetic variance, suggesting that volume measures from these regions may provide quantitative phenotypes to narrow the search for quantitative trait loci that influence brain structure.

STATISTICALLY ASSISTED FLUID IMAGE REGISTRATION ALGORITHM - SAFIRA.

In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry -a technique to analyze local volumetric differences in brain structure-applied to 46 3D brain scans from healthy monozygotic twins.

MULTIVARIATE VARIANCE-COMPONENTS ANALYSIS IN DTI.

Twin studies are a major research direction in imaging genetics, a new field, which combines algorithms from quantitative genetics and neuroimaging to assess genetic effects on the brain. In twin imaging studies, it is common to estimate the intraclass correlation (ICC), which measures the resemblance between twin pairs for a given phenotype. In this paper, we extend the commonly used Pearson correlation to a more appropriate definition, which uses restricted maximum likelihood methods (REML). We computed proportion of phenotypic variance due to additive (A) genetic factors, common (C) and unique (E) environmental factors using a new definition of the variance components in the diffusion tensor-valued signals. We applied our analysis to a dataset of Diffusion Tensor Images (DTI) from 25 identical and 25 fraternal twin pairs. Differences between the REML and Pearson estimators were plotted for different sample sizes, showing that the REML approach avoids severe biases when samples are smaller. Measures of genetic effects were computed for scalar and multivariate diffusion tensor derived measures including the geodesic anisotropy (tGA) and the full diffusion tensors (DT), revealing voxel-wise genetic contributions to brain fiber microstructure.