Nonpsychotic siblings of patients with childhood-onset schizophrenia (COS) share cortical gray matter abnormalities with their probands at an early age; these normalize by the time the siblings are aged 18 years, suggesting that the gray matter abnormalities in schizophrenia could be an age-specific endophenotype. Patients with COS also show significant white matter (WM) growth deficits, which have not yet been explored in nonpsychotic siblings.
To study WM growth differences in non-psychotic siblings of patients with COS.
Longitudinal (5-year) anatomic magnetic resonance imaging study mapping WM growth using a novel tensor-based morphometry analysis.
National Institutes of Health Clinical Center, Bethesda, Maryland.
Forty-nine healthy siblings of patients with COS (mean [SD] age, 16.1[5.3] years; 19 male, 30 female) and 57 healthy persons serving as controls (age, 16.9[5.3] years; 29 male, 28 female).
Magnetic resonance imaging.
Main Outcome Measure
White matter growth rates.
We compared the WM growth rates in 3 age ranges. In the youngest age group (7 to <14 years), we found a significant difference in growth rates, with siblings of patients with COS showing slower WM growth rates in the parietal lobes of the brain than age-matched healthy controls (false discovery rate, q = 0.05; critical P = .001 in the bilateral parietal WM; a post hoc analysis identified growth rate differences only on the left side, critical P =.004). A growth rate difference was not detectable at older ages. In 3-dimensional maps, growth rates in the siblings even appeared to surpass those of healthy individuals at later ages, at least locally in the brain, but this effect did not survive a multiple comparisons correction.
In this first longitudinal study of nonpsychotic siblings of patients with COS, the siblings showed early WM growth deficits, which normalized with age. As reported before for gray matter, WM growth may also be an age-specific endophenotype that shows compensatory normalization with age.
Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra’s algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer’s risk gene, CLU.
Structural connectivity; neuroimaging genetics; Dijkstra’s algorithm; HARDI tractography; path length
Numerous studies have demonstrated a sexual dimorphism of the human corpus callosum. However, the question remains if sex differences in brain size, which typically is larger in men than in women, or biological sex per se account for the apparent sex differences in callosal morphology. Comparing callosal dimensions between men and women matched for overall brain size may clarify the true contribution of biological sex, as any observed group difference should indicate pure sex effects. We thus examined callosal morphology in 24 male and 24 female brains carefully matched for overall size. In addition, we selected 24 extremely large male brains and 24 extremely small female brains to explore if observed sex effects might vary depending on the degree to which male and female groups differed in brain size. Using the individual T1-weighted brain images (n=96), we delineated the corpus callosum at midline and applied a well-validated surface-based mesh-modeling approach to compare callosal thickness at 100 equidistant points between groups determined by brain size and sex. The corpus callosum was always thicker in men than in women. However, this callosal sex difference was strongly determined by the cerebral sex difference overall. That is, the larger the discrepancy in brain size between men and women, the more pronounced the sex difference in callosal thickness, with hardly any callosal differences remaining between brain-size matched men and women. Altogether, these findings suggest that individual differences in brain size account for apparent sex differences in the anatomy of the corpus callosum.
Brain; Corpus Callosum; Gender; MRI; Sex
Alterations in gray matter (GM) density/ volume and cortical thickness (CT) have been demonstrated in small and heterogeneous samples of subjects with different chronic pain syndromes, including irritable bowel syndrome (IBS). Aggregating across 7 structural neuroimaging studies conducted at UCLA between August 2006 and April 2011, we examined group differences in regional GM volume in 201 predominantly premenopausal female subjects (82 IBS, mean age: 32 ± 10 SD, 119 Healthy Controls [HCs], 30± 10 SD). Applying graph theoretical methods and controlling for total brain volume, global and regional properties of large-scale structural brain networks were compared between IBS and HC groups. Relative to HCs, the IBS group had lower volumes in bilateral superior frontal gyrus, bilateral insula, bilateral amygdala, bilateral hippocampus, bilateral middle orbital frontal gyrus, left cingulate, left gyrus rectus, brainstem, and left putamen. Higher volume was found for the left postcentral gyrus. Group differences were no longer significant for most regions when controlling for Early Trauma Inventory global score with the exception of the right amygdala and the left post central gyrus. No group differences were found for measures of global and local network organization. Compared to HCs, the right cingulate gyrus and right thalamus were identified as significantly more critical for information flow. Regions involved in endogenous pain modulation and central sensory amplification were identified as network hubs in IBS. Overall, evidence for central alterations in IBS was found in the form of regional GM volume differences and altered global and regional properties of brain volumetric networks.
chronic pain; irritable bowel syndrome; gray matter volume; brain network analysis; graph theory
This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.
Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers.
We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).
We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.
Alzheimer's disease; Early-onset; ADNI; Mild cognitive impairment; Memory; Neuroimaging; Genetics
Neurobehavioral comorbidities are common in pediatric epilepsy with enduring adverse effects on functioning, but their neuroanatomical underpinning is unclear. Striatal and thalamic abnormalities have been associated with childhood-onset epilepsies, suggesting that epilepsy-related changes in the subcortical circuit might be associated with the combordities of children with epilepsy. We aimed to compare subcortical volumes and their relationship with age in children with complex partial seizures (CPS), childhood absence epilepsy (CAE), and healthy controls (HC). We examined the shared versus unique structural-functional relationships of these volumes with behavior problems, intelligence, language, peer interaction, and epilepsy variables in these two epilepsy syndromes.
We investigated volumetric differences of caudate, putamen, pallidum, and thalamus in children with CPS (N= 21), CAE (N=20), and HC (N=27). Study subjects underwent structural MRI, intelligence, and language testing. Parent-completed Child Behavior Checklists provided behavior problem and peer interaction scores. We examined the association of age, IQ, language, behavioral problems, and epilepsy variables with subcortical volumes that were significantly different between the children with epilepsy and HC.
Both children with CPS and CAE exhibited significantly smaller left thalamic volume compared to HC. In terms of developmental trajectory, greater thalamic volume was significantly correlated with increasing age in children with CPS and CAE but not in HC. With regard to the comorbidities, reduced left thalamic volumes were related to more social problems in children with CPS and CAE. Smaller left thalamic volumes in children with CPS were also associated with poor attention, lower IQ and language scores, and impaired peer interaction.
Our study is the first to directly compare and detect shared thalamic structural abnormalities in children with CPS and CAE. These findings highlight the vulnerability of the thalamus and provide important new insights on its possible role in the neurobehavioral comorbidities of childhood-onset epilepsy.
Characterization of the complex branching architecture of cerebral arteries across a representative sample of the human population is important for diagnosing, analyzing, and predicting pathological states. Brain arterial vasculature can be visualized by magnetic resonance angiography (MRA). However, most MRA studies are limited to qualitative assessments, partial morphometric analyses, individual (or small numbers of) subjects, proprietary datasets, or combinations of the above limitations. Neuroinformatics tools, developed for neuronal arbor analysis, were used to quantify vascular morphology from 3 T time-of-flight MRA high-resolution (620 μm isotropic) images collected in 61 healthy volunteers (36/25 F/M, average age = 31.2 ± 10.7, range = 19–64 years). We present in-depth morphometric analyses of the global and local anatomical features of these arbors. The overall structure and size of the vasculature did not significantly differ across genders, ages, or hemispheres. The total length of the three major arterial trees stemming from the circle of Willis (from smallest to largest: the posterior, anterior, and middle cerebral arteries; or PCAs, ACAs, and MCAs, respectively) followed an approximate 1:2:4 proportion. Arterial size co-varied across individuals: subjects with one artery longer than average tended to have all other arteries also longer than average. There was no net right–left difference across the population in any of the individual arteries, but ACAs were more lateralized than MCAs. MCAs, ACAs, and PCAs had similar branch-level properties such as bifurcation angles. Throughout the arterial vasculature, there were considerable differences between branch types: bifurcating branches were significantly shorter and straighter than terminating branches. Furthermore, the length and meandering of bifurcating branches increased with age and with path distance from the circle of Willis. All reconstructions are freely distributed through a public database to enable additional analyses and modeling (cng.gmu.edu/brava).
The NTRK3 gene (also known as TRKC) encodes a high affinity receptor for the neurotrophin 3′-nucleotidase (NT3), which is implicated in oligodendrocyte and myelin development. We previously found that white matter integrity in young adults related to genetic variants in genes encoding neurotrophins and their receptors. This underscores the importance of neurotrophins for white matter development. NTRK3 variants are putative risk factors for schizophrenia, bipolar disorder, and obsessive-compulsive disorder hoarding, suggesting that some NTRK3 variants may affect the brain.
To test this, we scanned 392 healthy adult twins and their siblings (mean age, 23.6 ± 2.2 years; range: 20-29 years) with 105-gradient 4-Tesla diffusion tensor imaging (DTI). We identified 18 single nucleotide polymorphisms (SNPs) in the NTRK3 gene that have been associated with neuropsychiatric disorders. We used a multi-SNP model, adjusting for family relatedness, age, and sex, to relate these variants to voxelwise fractional anisotropy (FA) – a DTI measure of white matter integrity.
FA was optimally predicted (based on the highest false discovery rate critical p), by five SNPs (rs1017412, rs2114252, rs16941261, rs3784406, and rs7176429; overall FDR critical p = 0.028). Gene effects were widespread and included the corpus callosum genu and inferior longitudinal fasciculus - regions implicated in several neuropsychiatric disorders and previously associated with other neurotrophin-related genetic variants in an overlapping sample of subjects. NTRK3 genetic variants, and neurotrophins more generally, may influence white matter integrity in brain regions implicated in neuropsychiatric disorders.
Fractional anisotropy; diffusion tensor imaging; single nucleotide polymorphism; schizophrenia; obsessive compulsive disorder; bipolar disorder
Brain connectivity declines in Alzheimer’s disease (AD), both functionally and structurally. Connectivity maps and networks derived from diffusion-based tractography offer new ways to track disease progression and to understand how AD affects the brain. Here we set out to identify (1) which fiber network measures show greatest differences between AD patients and controls, and (2) how these effects depend on the density of fibers extracted by the tractography algorithm. We computed brain networks from diffusion-weighted images (DWI) of the brain, in 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD). We derived connectivity matrices and network topology measures, for each subject, from whole-brain tractography and cortical parcellations. We used an ODF lookup table to speed up fiber extraction, and to exploit the full information in the orientation distribution function (ODF). This made it feasible to compute high density connectivity maps. We used accelerated tractography to compute a large number of fibers to understand what effect fiber density has on network measures and in distinguishing different disease groups in our data. We focused on global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity measures computed from weighted and binary undirected connectivity matrices. Of all these measures, the mean nodal degree best distinguished diagnostic groups. High-density fiber matrices were most helpful for picking up the more subtle clinical differences, e.g. between mild cognitively impaired (MCI) and normals, or for distinguishing subtypes of MCI (early versus late). Care is needed in clinical analyses of brain connectivity, as the density of extracted fibers may affect how well a network measure can pick up differences between patients and controls.
tractography; Hadoop; MapReduce; network measures; connectivity matrix; Alzheimer’s disease; ODF
Diffusion imaging can map anatomical connectivity in the living brain, offering new insights into fundamental questions such as how the left and right brain hemispheres differ. Anatomical brain asymmetries are related to speech and language abilities, but less is known about left/right hemisphere differences in brain wiring. To assess this, we scanned 457 young adults (age 23.4±2.0 SD years) and 112 adolescents (age 12-16) with 4-Tesla 105-gradient high-angular resolution diffusion imaging. We extracted fiber tracts throughout the brain with a Hough transform method. A 70×70 connectivity matrix was created, for each subject, based on the proportion of fibers intersecting 70 cortical regions. We identified significant differences in the proportions of fibers intersecting left and right hemisphere cortical regions. The degree of asymmetry in the connectivity matrices varied with age, as did the asymmetry in network topology measures such as the small-world effect.
tractography; high angular resolution diffusion imaging (HARDI); small-world effect; connectome; laterality
High angular resolution diffusion imaging (HARDI) allows in vivo analysis of the white matter structure and connectivity. Based on orientation distribution functions (ODFs) that represent the directionality of water diffusion at each point in the brain, tractography methods can recover major axonal pathways. This enables tract-based analysis of fiber integrity and connectivity. For multi-subject comparisons, fibers may be clustered into bundles that are consistently found across subjects. To do this, we scanned 20 young adults with HARDI at 4 T. From the reconstructed ODFs, we performed whole-brain tractography with a novel Hough transform method. We then used measures of agreement between the extracted 3D curves and a co-registered probabilistic DTI atlas to select key pathways. Using median filtering and a shortest path graph search, we derived the maximum density path to compactly represent each tract in the population. With this tract-based method, we performed tract-based analysis of fractional anisotropy, and assessed how the chosen tractography algorithm influenced the results. The resulting method may expedite population-based statistical analysis of HARDI and DTI.
tractography; clustering; Dijkstra’s shortest path; multi-subject analysis; fiber bundles
Multimodal brain image analysis : third International Workshop, MBIA 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013 : proceedings / Li Shen, Tianming Liu, Pew-Thian Yap, Heng Huang, Dinggang Shen, Carl-Fre.
In image-based medical research, atlases are widely used in many tasks, for example, spatial normalization and segmentation. If atlases are regarded as representative patterns for a population of images, then multiple atlases are required for a heterogeneous population. In conventional atlas construction methods, the “unit” of representative patterns is images. Every input image is associated with its most similar atlas. As the number of subjects increases, the heterogeneity increases accordingly, and a big number of atlases may be needed. In this paper, we explore using region-wise, instead of image-wise, patterns to represent a population. Different parts of an input image is fuzzily associated with different atlases according to voxel-level association weights. In this way, regional structure patterns from different atlases can be combined together. Based on this model, we design a variational framework for multi-atlas construction. In the application to two T1-weighted MRI data sets, the method shows promising performance, in comparison with a conventional unbiased atlas construction method.
Advances in diffusion weighted MR imaging have made it possible to non-invasively study the structral connectivity of human brains at high resolution. To model crossing fibers in white matter, a popular choice is the reconstruction of fiber orientation distributions (FODs) from diffusion data. For this sophiscated image representation of brain connectivity, classical image operations such as differentiation and interpolation must be redefined. In this paper, we introduce rotational gradient fields (RGF) as the spatial differential of FODs’ orientations. By taking into accout the rotational effect of traveling in the RGF, an FOD at one location can be transported and aligned with the FOD at the target location. We propose a method for inducing RGFs with FOD metrics. We also show how RGFs can be used for interpolation, yielding intuitively more reasonable results than the Frechet mean on the unit sphere in a Hilbert space .
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/).
Diffusion Tensor Imaging (DTI); Imaging genetics; Heritability; Meta-analysis; Multi-site; Reliability
Children with chromosome 22q11.2 Deletion Syndrome (22q11.2DS), Fragile X Syndrome (FXS), or Turner Syndrome (TS) are considered to belong to distinct genetic groups, as each disorder is caused by separate genetic alterations. Even so, they have similar cognitive and behavioral dysfunctions, particularly in visuospatial and numerical abilities. To assess evidence for common underlying neural microstructural alterations, we set out to determine whether these groups have partially overlapping white matter abnormalities, relative to typically developing controls. We scanned 101 female children between 7 and 14 years old: 25 with 22q11.2DS, 18 with FXS, 17 with TS, and 41 aged-matched controls using diffusion tensor imaging (DTI). Anisotropy and diffusivity measures were calculated and all brain scans were nonlinearly aligned to population and site-specific templates. We performed voxel-based statistical comparisons of the DTI-derived metrics between each disease group and the controls, while adjusting for age. Girls with 22q11.2DS showed lower fractional anisotropy (FA) than controls in the association fibers of the superior and inferior longitudinal fasciculi, the splenium of the corpus callosum, and the corticospinal tract. FA was abnormally lower in girls with FXS in the posterior limbs of the internal capsule, posterior thalami, and precentral gyrus. Girls with TS had lower FA in the inferior longitudinal fasciculus, right internal capsule and left cerebellar peduncle. Partially overlapping neurodevelopmental anomalies were detected in all three neurogenetic disorders. Altered white matter integrity in the superior and inferior longitudinal fasciculi and thalamic to frontal tracts may contribute to the behavioral characteristics of all of these disorders.
Diffussion Tensor Imaging; Genetic diseases; Neurodevelopmental diseases; Connectivity
Genetic analysis of diffusion tensor images (DTI) shows great promise in revealing specific genetic variants that affect brain integrity and connectivity. Most genetic studies of DTI analyze voxel-based diffusivity indices in the image space (such as 3D maps of fractional anisotropy) and overlook tract geometry. Here we propose an automated workflow to cluster fibers using a white matter probabilistic atlas and perform genetic analysis on the shape characteristics of fiber tracts. We apply our approach to large study of 4-Tesla high angular resolution diffusion imaging (HARDI) data from 198 healthy, young adult twins (age: 20–30). Illustrative results show heritability for the shapes of several major tracts, as color-coded maps.
HARDI; Tractography; Image Registration; White Matter Probabilistic Atlas; Fiber Alignment; Clustering; Curve Matching; Heritability
Multimodal brain image analysis : third International Workshop, MBIA 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013 : proceedings / Li Shen, Tianming Liu, Pew-Thian Yap, Heng Huang, Dinggang Shen, Carl-Fre.
We present a method for studying brain connectivity by simulating a dynamical evolution of the nodes of the network. The nodes are treated as particles, and evolved under a simulated force analogous to gravitational acceleration in the well-known N -body problem. The particle nodes correspond to regions of the cortex. The locations of particles are defined as the centers of the respective regions on the cortex and their masses are proportional to each region’s volume. The force of attraction is modeled on the gravitational force, and explicitly made proportional to the elements of a connectivity matrix derived from diffusion imaging data. We present experimental results of the simulation on a population of 110 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), consisting of healthy elderly controls, early mild cognitively impaired (eMCI), late MCI (LMCI), and Alzheimer’s disease (AD) patients. Results show significant differences in the dynamic properties of connectivity networks in healthy controls, compared to eMCI as well as AD patients.
gravity; n-body simulation; diffusion; connectivity; MRI
Advanced diffusion weighted MR imaging allows non-invasive study on the structural connectivity of human brains. Fiber orientation distributions (FODs) reconstructed from diffusion data are a popular model to represent crossing fibers. For this sophisticated image representation of connectivity, classical image operations such as smoothing must be redefined. In this paper, we propose a novel rotation-induced Riemannian metric for FODs, and introduce a weighted diffusion process for FODs regarding this Riemannian manifold. We show how this Riemannian manifold can be used for smoothing, interpolation and building image-pyramids, yielding more accurate or intuitively more reasonable results than the linear or the unit hyper-sphere manifold.
Diffusion weighted magnetic resonance imaging (DW-MRI) are now widely used to assess brain integrity in clinical populations. The growing interest in mapping brain connectivity has made it vital to consider what scanning parameters affect the accuracy, stability, and signal-to-noise of Diffusion measures. Trade-offs between scan parameters can only be optimized if their effects on various commonly derived measures are better understood. To explore angular versus spatial resolution trade-offs in standard tensor-derived measures, and in measures that use the full angular information in diffusion signal, we scanned eight subjects twice, two weeks apart, using three protocols that took the same amount of time (7 minutes). Scans with 3, 2.7, 2.5 mm isotropic voxels were collected using 48, 41, and 37 diffusion-sensitized gradients to equalize scan times. A specially designed DTI phantom was also scanned with the same protocols, and different b-values. We assessed how several diffusion measures including fractional anisotropy (FA), mean diffusivity (MD), and the full 3D orientation distribution function (ODF) depended on the spatial/angular resolution and the SNR. We also created maps of stability over time in the FA, MD, ODF, skeleton FA of 14 TBSS-derived ROIs, and an information uncertainty index derived from the tensor distribution function, which models the signal using a continuous mixture of tensors. In scans of the same duration, higher angular resolution and larger voxels boosted SNR and improved stability over time. The increased partial voluming in large voxels also led to bias in estimating FA, but this was partially addressed by using “beyond-tensor” models of diffusion.
High Angular Resolution Diffusion Imaging; Diffusion Tensor Imaging; Spatial Resolution; Angular Resolution; Orientation Distribution Function; Tensor Distribution Function; reproducibility
Segmenting brain from non-brain tissue within magnetic resonance (MR) images of the human head, also known as skull-stripping, is a critical processing step in the analysis of neuroimaging data. Though many algorithms have been developed to address this problem, challenges remain. In this paper, we apply the “deformable organism” framework to the skull-stripping problem. Within this framework, deformable models are equipped with higher-level control mechanisms based on the principles of artificial life, including sensing, reactive behavior, knowledge representation, and proactive planning. Our new deformable organisms are governed by a high-level plan aimed at the fully-automated segmentation of various parts of the head in MR imagery, and they are able to cooperate in computing a robust and accurate segmentation. We applied our segmentation approach to a test set of human MRI data using manual delineations of the data as a reference “gold standard.” We compare these results with results from three widely used methods using set-similarity metrics.
deformable organisms; skull-stripping; MRI; deformable models; segmentation
Numerous studies suggest that interhemispheric inhibition – relayed via the corpus callosum – plays an important role in unilateral hand motions. Interestingly, transcallosal inhibition appears to be indicative of a strong laterality effect, where generally the dominant hemisphere exerts inhibition on the non-dominant one. These effects have been largely identified through functional studies in adult populations, but links between motor performance and callosal structure (especially during sensitive periods of neurodevelopment) remain largely unknown. We therefore investigated correlations between Purdue Pegboard performance (a test of motor function) and local callosal thickness in 170 right-handed children and adolescents (mean age: 11.5 ± 3.4 years; range: 6–17 years). Better task performance with the right (dominant) hand was associated with greater callosal thickness in isthmus and posterior midbody. Task performance using both hands yielded smaller and less significant correlations in the same regions, while task performance using the left (non-dominant) hand showed no significant correlations with callosal thickness. There were no significant interactions with age and sex. These links between motor performance and callosal structure may constitute the neural correlate of interhemispheric inhibition, which is thought to be necessary for fast and complex unilateral motions and to be biased towards the dominant hand.
age; gender; interhemispheric inhibition; motor; Pegboard; sex
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.
Alzheimer's disease; Mild cognitive impairment; Amyloid; Tau; Biomarker
In an attempt to increase power to detect genetic associations with brain phenotypes derived from human neuroimaging data, we recently conducted a large-scale genome-wide association meta-analysis of hippocampal, brain, and intracranial volume through the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Here we present a freely-available online interactive tool, EnigmaVis, which makes it easy to visualize the association results generated by the consortium alongside allele frequency, genes, and functional annotations. EnigmaVis runs natively within the web browser, and generates plots that show the level of association between brain phenotypes at user-specified genomic positions. Uniquely, EnigmaVis is dynamic; users can interact with elements on the plot in real time. This software will be useful when exploring the effect on brain structure of particular genetic variants influencing neuropsychiatric illness and cognitive function. Future projects of the consortium and updates to EnigmaVis will also be displayed on the site. EnigmaVis is freely available online at http://enigma.loni.ucla.edu/enigma-vis/.
Autism spectrum disorder (ASD) is a heterogeneous disorder of brain development with wide-ranging cognitive deficits. Typically diagnosed before age 3, ASD is behaviorally defined but patients are thought to have protracted alterations in brain maturation. With longitudinal magnetic resonance imaging (MRI), we mapped an anomalous developmental trajectory of the brains of autistic compared to those of typically developing children and adolescents. Using tensor-based morphometry (TBM), we created 3D maps visualizing regional tissue growth rates based on longitudinal brain MRI scans of 13 autistic and 7 typically developing boys (mean age/inter-scan interval: autism 12.0 ± 2.3 years/2.9 ± 0.9 years; control 12.3 ± 2.4/2.8 ± 0.8). The typically developing boys demonstrated strong whole-brain white matter growth during this period, but the autistic boys showed abnormally slowed white matter development (p = 0.03, corrected), especially in the parietal (p = 0.008), temporal (p = 0.03) and occipital lobes (p =0.02). We also visualized abnormal overgrowth in autism in some gray matter structures, such as the putamen and anterior cingulate cortex. Our findings reveal aberrant growth rates in brain regions implicated in social impairment, communication deficits and repetitive behaviors in autism, suggesting that growth rate abnormalities persist into adolescence. TBM revealed persisting growth rate anomalies long after diagnosis, which has implications for evaluation of therapeutic effects.
Autism spectrum disorder; longitudinal; MRI; tensor-based morphometry; development; white matter
Recent findings suggest a close link between long-term meditation practices and the structure of the corpus callosum. Prior analyses, however, have focused on estimating mean fractional anisotropy (FA) within two large pre-defined callosal tracts only. Additional effects might exist in other, non-explored callosal regions and/or with respect to callosal attributes not captured by estimates of FA. To further explore callosal features in the framework of meditation, we analyzed 30 meditators and 30 controls, carefully matched for sex, age, and handedness. We applied a multimodal imaging approach using diffusion tensor imaging (DTI) in combination with structural magnetic resonance imaging (MRI). Callosal measures of tract-specific FA were complemented with other global (segment-specific) estimates as well as extremely local (point-wise) measures of callosal micro- and macro-structure. Callosal measures were larger in long-term meditators compared to controls, particularly in anterior callosal sections. However, differences achieved significance only when increasing the regional sensitivity of the measurement (i.e., using point-wise measures versus segment-specific measures) and were more prominent for microscopic than macroscopic characteristics (i.e., callosal FA versus callosal thickness). Thicker callosal regions and enhanced FA in meditators might indicate greater connectivity, possibly reflecting increased hemispheric integration during cerebral processes involving (pre)frontal regions. Such a brain organization might be linked to achieving characteristic mental states and skills as associated with meditation, though this hypothesis requires behavioral confirmation. Moreover, longitudinal studies are required to address whether the observed callosal effects are induced by meditation or constitute an innate prerequisite for the start or successful continuation of meditation.
brain; corpus callosum; DTI; mindfulness; MRI; plasticity