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1.  Genetics of Path Lengths in Brain Connectivity Networks: HARDI-Based Maps in 457 Adults 
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.
PMCID: PMC4288784  PMID: 25584366
Structural connectivity; neuroimaging genetics; Dijkstra’s algorithm; HARDI tractography; path length
2.  Registering Cortical Surfaces Based on Whole-Brain Structural Connectivity and Continuous Connectivity Analysis 
We present a framework for registering cortical surfaces based on tractography-informed structural connectivity. We define connectivity as a continuous kernel on the product space of the cortex, and develop a method for estimating this kernel from tractography fiber models. Next, we formulate the kernel registration problem, and present a means to non-linearly register two brains’ continuous connectivity profiles. We apply theoretical results from operator theory to develop an algorithm for decomposing the connectome into its shared and individual components. Lastly, we extend two discrete connectivity measures to the continuous case, and apply our framework to 98 Alzheimer’s patients and controls. Our measures show significant differences between the two groups.
PMCID: PMC4283762  PMID: 25320795
Diffusion MRI; Cortical Surface Registration; Connectivity Analysis; Data Fusion
3.  Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines 
Imaging genetics is an emerging methodological field that combines genetic information with medical imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an imaging genetics study, it must be heritable: at least some proportion of its variance must be due to genetic influences. The Sequential Oligogenic Linkage Analysis Routines (SOLAR) imaging genetics software can estimate the heritability of a trait in complex pedigrees. We investigate the ability of SOLAR to accurately estimate heritability and common environmental effects on simulated imaging phenotypes in various family structures. We found that heritability is reliably estimated with small family-based studies of 40 to 80 individuals, though subtle differences remain between the family structures. In an imaging application analysis, we found that with 80 subjects in any of the family structures, estimated heritability of white matter fractional anisotropy was biased by <10% for every region of interest. Results from these studies can be used when investigators are evaluating power in planning genetic analyzes.
PMCID: PMC4281883  PMID: 25558465
heritability; imaging genetics; power calculation; statistical analysis
4.  Relation between variants in the neurotrophin receptor gene, NTRK3, and white matter integrity in healthy young adults 
NeuroImage  2013;82:146-153.
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.
PMCID: PMC3948328  PMID: 23727532
Fractional anisotropy; diffusion tensor imaging; single nucleotide polymorphism; schizophrenia; obsessive compulsive disorder; bipolar disorder
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.
PMCID: PMC4232939  PMID: 25404993
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.
PMCID: PMC4232949  PMID: 25404992
tractography; clustering; Dijkstra’s shortest path; multi-subject analysis; fiber bundles
7.  Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group 
NeuroImage  2013;81:455-469.
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 (
PMCID: PMC3729717  PMID: 23629049
Diffusion Tensor Imaging (DTI); Imaging genetics; Heritability; Meta-analysis; Multi-site; Reliability
8.  White matter microstructural abnormalities in girls with chromosome 22q11.2 deletion syndrome, Fragile X or Turner syndrome as evidenced by diffusion tensor imaging 
NeuroImage  2013;81:441-454.
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.
PMCID: PMC3947617  PMID: 23602925
Diffussion Tensor Imaging; Genetic diseases; Neurodevelopmental diseases; Connectivity
9.  Heritability of White Matter Fiber Tract Shapes: A HARDI Study of 198 Twins* 
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.
PMCID: PMC4205954  PMID: 25346947
HARDI; Tractography; Image Registration; White Matter Probabilistic Atlas; Fiber Alignment; Clustering; Curve Matching; Heritability
10.  Genetics of the Connectome 
NeuroImage  2013;80:475-488.
Connectome genetics attempts to discover how genetic factors affect brain connectivity. Here we review a variety of genetic analysis methods – such as genome-wide association studies (GWAS), linkage and candidate gene studies – that have been fruitfully adapted to imaging data to implicate specific variants in the genome for brain-related traits. We then review studies of that emphasized the genetic influences on brain connectivity. Some of these perform genetic analysis of brain integrity and connectivity using diffusion MRI, and others have mapped genetic effects on functional networks using resting state functional MRI. Connectome-wide genome-wide scans have also been conducted, and we review the multivariate methods required to handle the extremely high dimension of genomic and the network data. We also review some consortium efforts, such as ENIGMA, that offer the power to detect robust common genetic associations using phenotypic harmonization procedures and meta-analysis. Current work on connectome genetics is advancing on many fronts and promises to shed light on how disease risk genes affect the brain. It is already discovering new genetic loci and even entire genetic networks that affect brain organization and connectivity.
PMCID: PMC3905600  PMID: 23707675
11.  Angular versus spatial resolution trade-offs for diffusion imaging under time constraints 
Human brain mapping  2012;34(10):2688-2706.
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.
PMCID: PMC3468661  PMID: 22522814
High Angular Resolution Diffusion Imaging; Diffusion Tensor Imaging; Spatial Resolution; Angular Resolution; Orientation Distribution Function; Tensor Distribution Function; reproducibility
12.  Breakdown of Brain Connectivity Between Normal Aging and Alzheimer's Disease: A Structural k-Core Network Analysis 
Brain Connectivity  2013;3(4):407-422.
Brain connectivity analyses show considerable promise for understanding how our neural pathways gradually break down in aging and Alzheimer's disease (AD). Even so, we know very little about how the brain's networks change in AD, and which metrics are best to evaluate these changes. To better understand how AD affects brain connectivity, we analyzed anatomical connectivity based on 3-T diffusion-weighted images from 111 subjects (15 with AD, 68 with mild cognitive impairment, and 28 healthy elderly; mean age, 73.7±7.6 SD years). We performed whole brain tractography based on the orientation distribution functions, and compiled connectivity matrices showing the proportions of detected fibers interconnecting 68 cortical regions. We computed a variety of measures sensitive to anatomical network topology, including the structural backbone—the so-called “k-core”—of the anatomical network, and the nodal degree. We found widespread network disruptions, as connections were lost in AD. Among other connectivity measures showing disease effects, network nodal degree, normalized characteristic path length, and efficiency decreased with disease, while normalized small-worldness increased, in the whole brain and left and right hemispheres individually. The normalized clustering coefficient also increased in the whole brain; we discuss factors that may cause this effect. The proportions of fibers intersecting left and right cortical regions were asymmetrical in all diagnostic groups. This asymmetry may intensify as disease progressed. Connectivity metrics based on the k-core may help understand brain network breakdown as cognitive impairment increases, revealing how degenerative diseases affect the human connectome.
PMCID: PMC3749712  PMID: 23701292
Alzheimer's disease; asymmetry; brain connectivity; diffusion tensor imaging; efficiency; k-core; mild cognitive impairment; nodal degree; small-world; tractography
13.  Exhaustive search of the SNP-SNP interactome identifies epistatic effects on brain volume in two cohorts 
The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics mainly due to the complexity of conducting ∼1011 pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much larger than the number of observations. Using an implementation of the SIS algorithm (called EPISIS), we used exhaustive search of the genome-wide, SNP-SNP interactome to identify and prioritize SNPs for interaction analysis. We identified a significant SNP pair, rs1345203 and rs1213205, associated with temporal lobe volume. We further examined the full-brain, voxelwise effects of the interaction in the ADNI dataset and separately in an independent dataset of healthy twins (QTIM). We found that each additional loading in the epistatic effect was associated with ∼5% greater brain regional brain volume (a protective effect) in both the ADNI and QTIM samples.
PMCID: PMC4109883  PMID: 24505811
epistasis; interaction; genome; sure independence; tensor-based morphometry
14.  Alzheimer's Disease Disrupts Rich Club Organization in Brain Connectivity Networks 
Diffusion imaging and brain connectivity analyses can monitor white matter deterioration, revealing how neural pathways break down in aging and Alzheimer's disease (AD). Here we tested how AD disrupts the ‘rich club’ effect – a network property found in the normal brain – where high-degree nodes in the connectivity network are more heavily interconnected with each other than expected by chance. We analyzed 3-Tesla whole-brain diffusionweighted images (DWI) from 66 subjects (22 AD/44 normal elderly). We performed whole-brain tractography based on the orientation distribution functions. Connectivity matrices were compiled, representing the proportion of detected fibers interconnecting 68 cortical regions. As expected, AD patients had a lower nodal degree (average number of connections) in cortical regions implicated in the disease. Unexpectedly, the normalized rich club coefficient was higher in AD. AD disrupts cortical networks by removing connections; when these networks are thresholded, organizational properties are disrupted leading to additional new biomarkers of AD.
PMCID: PMC4063983  PMID: 24953139
15.  A Commonly Carried Genetic Variant in the Delta Opioid Receptor Gene, OPRD1, is Associated with Smaller Regional Brain Volumes: Replication in Elderly and Young Populations 
Human brain mapping  2013;35(4):1226-1236.
Delta opioid receptors are implicated in a variety of psychiatric and neurological disorders. These receptors play a key role in the reinforcing properties of drugs of abuse, and polymorphisms in OPRD1 (the gene encoding delta opioid receptors) are associated with drug addiction. Delta opioid receptors are also involved in protecting neurons against hypoxic and ischemic stress. Here, we first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer’s Disease Neuroimaging Initiative. We hypothesized that common variants in OPRD1 would be associated with differences in brain structure, particularly in regions relevant to addictive and neurodegenerative disorders. One very common variant (rs678849) predicted differences in regional brain volumes. We replicated the association of this single-nucleotide polymorphism with regional tissue volumes in a large sample of young participants in the Queensland Twin Imaging study. Although the same allele was associated with reduced volumes in both cohorts, the brain regions affected differed between the two samples. In healthy elderly, exploratory analyses suggested that the genotype associated with reduced brain volumes in both cohorts may also predict cerebrospinal fluid levels of neurodegenerative biomarkers, but this requires confirmation. If opiate receptor genetic variants are related to individual differences in brain structure, genotyping of these variants may be helpful when designing clinical trials targeting delta opioid receptors to treat neurological disorders.
PMCID: PMC4046708  PMID: 23427138
neuroimaging; genetics; neurodegeneration; drug addiction; opiates
16.  Genome-wide association identifies genetic variants associated with lentiform nucleus volume in N=1345 young and elderly subjects 
Brain imaging and behavior  2013;7(2):102-115.
Deficits in lentiform nucleus volume and morphometry are implicated in a number of genetically influenced disorders, including Parkinson’s disease, schizophrenia, and ADHD. Here we performed genome-wide searches to discover common genetic variants associated with differences in lentiform nucleus volume in human populations. We assessed structural MRI scans of the brain in two large genotyped samples: the Alzheimer’s Disease Neuroimaging Initiative (ADNI; N=706) and the Queensland Twin Imaging Study (QTIM; N=639). Statistics of association from each cohort were combined meta-analytically using a fixed-effects model to boost power and to reduce the prevalence of false positive findings. We identified a number of associations in and around the flavin-containing monooxygenase (FMO) gene cluster. The most highly associated SNP, rs1795240, was located in the FMO3 gene; after meta-analysis, it showed genome-wide significant evidence of association with lentiform nucleus volume (PMA=4.79×10−8). This commonly-carried genetic variant accounted for 2.68 % and 0.84 % of the trait variability in the ADNI and QTIM samples, respectively, even though the QTIM sample was on average 50 years younger. Pathway enrichment analysis revealed significant contributions of this gene to the cytochrome P450 pathway, which is involved in metabolizing numerous therapeutic drugs for pain, seizures, mania, depression, anxiety, and psychosis. The genetic variants we identified provide replicated, genome-wide significant evidence for the FMO gene cluster’s involvement in lentiform nucleus volume differences in human populations.
PMCID: PMC3779070  PMID: 22903471
Basal ganglia; Genome-wide association study (GWAS); MRI; Replication; Morphometry; Drug metabolism
17.  Test-Retest Reliability of Graph Theory Measures of Structural Brain Connectivity 
The human connectome has recently become a popular research topic in neuroscience, and many new algorithms have been applied to analyze brain networks. In particular, network topology measures from graph theory have been adapted to analyze network efficiency and ‘small-world’ properties. While there has been a surge in the number of papers examining connectivity through graph theory, questions remain about its test-retest reliability (TRT). In particular, the reproducibility of structural connectivity measures has not been assessed. We examined the TRT of global connectivity measures generated from graph theory analyses of 17 young adults who underwent two high-angular resolution diffusion (HARDI) scans approximately 3 months apart. Of the measures assessed, modularity had the highest TRT, and it was stable across a range of sparsities (a thresholding parameter used to define which network edges are retained). These reliability measures underline the need to develop network descriptors that are robust to acquisition parameters.
PMCID: PMC4039303  PMID: 23286144
18.  Development of Insula Connectivity Between Ages 12 and 30 Revealed by High Angular Resolution Diffusion Imaging 
Human brain mapping  2013;35(4):1790-1800.
The insula, hidden deep within the Sylvian fissures, has proven difficult to study from a connectivity perspective. Most of our current information on the anatomical connectivity of the insula comes from studies of nonhuman primates and post mortem human dissections. To date, only two neuroimaging studies have successfully examined the connectivity of the insula. Here we examine how the connectivity of the insula develops between ages 12 and 30, in 307 young adolescent and adult subjects scanned with 4-Tesla high angular resolution diffusion imaging (HARDI). The density of fiber connections between the insula and the frontal and parietal cortex decreased with age, but the connection density between the insula and the temporal cortex generally increased with age. This trajectory is in line with well-known patterns of cortical development in these regions. In addition, males and females showed different developmental trajectories for the connection between the left insula and the left precentral gyrus. The insula plays many different roles, some of them affected in neuropsychiatric disorders; this information on the insula's connectivity may help efforts to elucidate mechanisms of brain disorders in which it is implicated.
PMCID: PMC4017914  PMID: 23836455
insula; development; tractography; HARDI; structural connectivity
The ‘rich club’ coefficient describes a phenomenon where a network's hubs (high-degree nodes) are on average more intensely interconnected than lower-degree nodes. Networks with rich clubs often have an efficient, higher-order organization, but we do not yet know how the rich club emerges in the living brain, or how it changes as our brain networks develop. Here we chart the developmental trajectory of the rich club in anatomical brain networks from 438 subjects aged 12-30. Cortical networks were constructed from 68×68 connectivity matrices of fiber density, using whole-brain tractography in 4-Tesla 105-gradient high angular resolution diffusion images (HARDI). The adult and younger cohorts had rich clubs that included different nodes; the rich club effect intensified with age. Rich-club organization is a sign of a network's efficiency and robustness. These concepts and findings may be advantageous for studying brain maturation and abnormal brain development.
PMCID: PMC4017916  PMID: 24827471
rich club coefficient; high angular resolution diffusion imaging (HARDI); tractography; network analyses; development; structural connectivity
20.  Magnetic Resonance Field Strength Effects on Diffusion Measures and Brain Connectivity Networks 
Brain Connectivity  2013;3(1):72-86.
The quest to map brain connectivity is being pursued worldwide using diffusion imaging, among other techniques. Even so, we know little about how brain connectivity measures depend on the magnetic field strength of the scanner. To investigate this, we scanned 10 healthy subjects at 7 and 3 tesla—using 128-gradient high-angular resolution diffusion imaging. For each subject and scan, whole-brain tractography was used to estimate connectivity between 113 cortical and subcortical regions. We examined how scanner field strength affects (i) the signal-to-noise ratio (SNR) of the non-diffusion-sensitized reference images (b0); (ii) diffusion tensor imaging (DTI)-derived fractional anisotropy (FA), mean, radial, and axial diffusivity (MD/RD/AD), in atlas-defined regions; (iii) whole-brain tractography; (iv) the 113×113 brain connectivity maps; and (v) five commonly used network topology measures. We also assessed effects of the multi-channel reconstruction methods (sum-of-squares, SOS, at 7T; adaptive recombine, AC, at 3T). At 7T with SOS, the b0 images had 18.3% higher SNR than with 3T-AC. FA was similar for most regions of interest (ROIs) derived from an online DTI atlas (ICBM81), but higher at 7T in the cerebral peduncle and internal capsule. MD, AD, and RD were lower at 7T for most ROIs. The apparent fiber density between some subcortical regions was greater at 7T-SOS than 3T-AC, with a consistent connection pattern overall. Suggesting the need for caution, the recovered brain network was apparently more efficient at 7T, which cannot be biologically true as the same subjects were assessed. Care is needed when comparing network measures across studies, and when interpreting apparently discrepant findings.
PMCID: PMC3621300  PMID: 23205551
brain network analysis; DTI; fractional anisotropy; graph theory; high-field MRI; high angular resolution diffusion imaging (HARDI); signal-to-noise ratio; tractography
21.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data 
Thompson, Paul M. | Stein, Jason L. | Medland, Sarah E. | Hibar, Derrek P. | Vasquez, Alejandro Arias | Renteria, Miguel E. | Toro, Roberto | Jahanshad, Neda | Schumann, Gunter | Franke, Barbara | Wright, Margaret J. | Martin, Nicholas G. | Agartz, Ingrid | Alda, Martin | Alhusaini, Saud | Almasy, Laura | Almeida, Jorge | Alpert, Kathryn | Andreasen, Nancy C. | Andreassen, Ole A. | Apostolova, Liana G. | Appel, Katja | Armstrong, Nicola J. | Aribisala, Benjamin | Bastin, Mark E. | Bauer, Michael | Bearden, Carrie E. | Bergmann, Ørjan | Binder, Elisabeth B. | Blangero, John | Bockholt, Henry J. | Bøen, Erlend | Bois, Catherine | Boomsma, Dorret I. | Booth, Tom | Bowman, Ian J. | Bralten, Janita | Brouwer, Rachel M. | Brunner, Han G. | Brohawn, David G. | Buckner, Randy L. | Buitelaar, Jan | Bulayeva, Kazima | Bustillo, Juan R. | Calhoun, Vince D. | Cannon, Dara M. | Cantor, Rita M. | Carless, Melanie A. | Caseras, Xavier | Cavalleri, Gianpiero L. | Chakravarty, M. Mallar | Chang, Kiki D. | Ching, Christopher R. K. | Christoforou, Andrea | Cichon, Sven | Clark, Vincent P. | Conrod, Patricia | Coppola, Giovanni | Crespo-Facorro, Benedicto | Curran, Joanne E. | Czisch, Michael | Deary, Ian J. | de Geus, Eco J. C. | den Braber, Anouk | Delvecchio, Giuseppe | Depondt, Chantal | de Haan, Lieuwe | de Zubicaray, Greig I. | Dima, Danai | Dimitrova, Rali | Djurovic, Srdjan | Dong, Hongwei | Donohoe, Gary | Duggirala, Ravindranath | Dyer, Thomas D. | Ehrlich, Stefan | Ekman, Carl Johan | Elvsåshagen, Torbjørn | Emsell, Louise | Erk, Susanne | Espeseth, Thomas | Fagerness, Jesen | Fears, Scott | Fedko, Iryna | Fernández, Guillén | Fisher, Simon E. | Foroud, Tatiana | Fox, Peter T. | Francks, Clyde | Frangou, Sophia | Frey, Eva Maria | Frodl, Thomas | Frouin, Vincent | Garavan, Hugh | Giddaluru, Sudheer | Glahn, David C. | Godlewska, Beata | Goldstein, Rita Z. | Gollub, Randy L. | Grabe, Hans J. | Grimm, Oliver | Gruber, Oliver | Guadalupe, Tulio | Gur, Raquel E. | Gur, Ruben C. | Göring, Harald H. H. | Hagenaars, Saskia | Hajek, Tomas | Hall, Geoffrey B. | Hall, Jeremy | Hardy, John | Hartman, Catharina A. | Hass, Johanna | Hatton, Sean N. | Haukvik, Unn K. | Hegenscheid, Katrin | Heinz, Andreas | Hickie, Ian B. | Ho, Beng-Choon | Hoehn, David | Hoekstra, Pieter J. | Hollinshead, Marisa | Holmes, Avram J. | Homuth, Georg | Hoogman, Martine | Hong, L. Elliot | Hosten, Norbert | Hottenga, Jouke-Jan | Hulshoff Pol, Hilleke E. | Hwang, Kristy S. | Jack, Clifford R. | Jenkinson, Mark | Johnston, Caroline | Jönsson, Erik G. | Kahn, René S. | Kasperaviciute, Dalia | Kelly, Sinead | Kim, Sungeun | Kochunov, Peter | Koenders, Laura | Krämer, Bernd | Kwok, John B. J. | Lagopoulos, Jim | Laje, Gonzalo | Landen, Mikael | Landman, Bennett A. | Lauriello, John | Lawrie, Stephen M. | Lee, Phil H. | Le Hellard, Stephanie | Lemaître, Herve | Leonardo, Cassandra D. | Li, Chiang-shan | Liberg, Benny | Liewald, David C. | Liu, Xinmin | Lopez, Lorna M. | Loth, Eva | Lourdusamy, Anbarasu | Luciano, Michelle | Macciardi, Fabio | Machielsen, Marise W. J. | MacQueen, Glenda M. | Malt, Ulrik F. | Mandl, René | Manoach, Dara S. | Martinot, Jean-Luc | Matarin, Mar | Mather, Karen A. | Mattheisen, Manuel | Mattingsdal, Morten | Meyer-Lindenberg, Andreas | McDonald, Colm | McIntosh, Andrew M. | McMahon, Francis J. | McMahon, Katie L. | Meisenzahl, Eva | Melle, Ingrid | Milaneschi, Yuri | Mohnke, Sebastian | Montgomery, Grant W. | Morris, Derek W. | Moses, Eric K. | Mueller, Bryon A. | Muñoz Maniega, Susana | Mühleisen, Thomas W. | Müller-Myhsok, Bertram | Mwangi, Benson | Nauck, Matthias | Nho, Kwangsik | Nichols, Thomas E. | Nilsson, Lars-Göran | Nugent, Allison C. | Nyberg, Lars | Olvera, Rene L. | Oosterlaan, Jaap | Ophoff, Roel A. | Pandolfo, Massimo | Papalampropoulou-Tsiridou, Melina | Papmeyer, Martina | Paus, Tomas | Pausova, Zdenka | Pearlson, Godfrey D. | Penninx, Brenda W. | Peterson, Charles P. | Pfennig, Andrea | Phillips, Mary | Pike, G. Bruce | Poline, Jean-Baptiste | Potkin, Steven G. | Pütz, Benno | Ramasamy, Adaikalavan | Rasmussen, Jerod | Rietschel, Marcella | Rijpkema, Mark | Risacher, Shannon L. | Roffman, Joshua L. | Roiz-Santiañez, Roberto | Romanczuk-Seiferth, Nina | Rose, Emma J. | Royle, Natalie A. | Rujescu, Dan | Ryten, Mina | Sachdev, Perminder S. | Salami, Alireza | Satterthwaite, Theodore D. | Savitz, Jonathan | Saykin, Andrew J. | Scanlon, Cathy | Schmaal, Lianne | Schnack, Hugo G. | Schork, Andrew J. | Schulz, S. Charles | Schür, Remmelt | Seidman, Larry | Shen, Li | Shoemaker, Jody M. | Simmons, Andrew | Sisodiya, Sanjay M. | Smith, Colin | Smoller, Jordan W. | Soares, Jair C. | Sponheim, Scott R. | Sprooten, Emma | Starr, John M. | Steen, Vidar M. | Strakowski, Stephen | Strike, Lachlan | Sussmann, Jessika | Sämann, Philipp G. | Teumer, Alexander | Toga, Arthur W. | Tordesillas-Gutierrez, Diana | Trabzuni, Daniah | Trost, Sarah | Turner, Jessica | Van den Heuvel, Martijn | van der Wee, Nic J. | van Eijk, Kristel | van Erp, Theo G. M. | van Haren, Neeltje E. M. | van ‘t Ent, Dennis | van Tol, Marie-Jose | Valdés Hernández, Maria C. | Veltman, Dick J. | Versace, Amelia | Völzke, Henry | Walker, Robert | Walter, Henrik | Wang, Lei | Wardlaw, Joanna M. | Weale, Michael E. | Weiner, Michael W. | Wen, Wei | Westlye, Lars T. | Whalley, Heather C. | Whelan, Christopher D. | White, Tonya | Winkler, Anderson M. | Wittfeld, Katharina | Woldehawariat, Girma | Wolf, Christiane | Zilles, David | Zwiers, Marcel P. | Thalamuthu, Anbupalam | Schofield, Peter R. | Freimer, Nelson B. | Lawrence, Natalia S. | Drevets, Wayne
Brain Imaging and Behavior  2014;8(2):153-182.
The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
PMCID: PMC4008818  PMID: 24399358
Genetics; MRI; GWAS; Consortium; Meta-analysis; Multi-site
22.  Development of Brain Structural Connectivity between Ages 12 and 30: A 4-Tesla Diffusion Imaging Study in 439 Adolescents and Adults 
NeuroImage  2012;64:671-684.
Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain’s anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12 year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16 year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4 Tesla. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant’s co-registered anatomical scans. The degree of fiber connections between all pairs of cortical regions, or nodes, were found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70×70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.
PMCID: PMC3603574  PMID: 22982357
HARDI; structural connectivity; graph theory; development
23.  Disrupted Brain Networks in the Aging HIV+ Population 
Brain Connectivity  2012;2(6):335-344.
Antiretroviral therapies have become widely available, and as a result, individuals infected with the human immunodeficiency virus (HIV) are living longer, and becoming integrated into the geriatric population. Around half of the HIV+ population shows some degree of cognitive impairment, but it is unknown how their neural networks and brain connectivity compare to those of noninfected people. Here we combined magnetic resonance imaging-based cortical parcellations with high angular resolution diffusion tensor imaging tractography in 55 HIV-seropositive patients and 30 age-matched controls, to map white matter connections between cortical regions. We set out to determine selective virus-associated disruptions in the brain's structural network. All individuals in this study were aged 60–80, with full access to antiretroviral therapy. Frontal and motor connections were compromised in HIV+ individuals. HIV+ people who carried the apolipoprotein E4 allele (ApoE4) genotype—which puts them at even greater risk for neurodegeneration—showed additional network structure deficits in temporal and parietal connections. The ApoE4 genotype interacted with duration of illness. Carriers showed greater brain network inefficiencies the longer they were infected. Neural network deficiencies in HIV+ populations exceed those typical of normal aging, and are worse in those genetically predisposed to brain degeneration. This work isolates neuropathological alterations in HIV+ elders, even when treated with antiretroviral therapy. Network impairments may contribute to the neuropsychological abnormalities in elderly HIV patients, who will soon account for around half of all HIV+ adults.
PMCID: PMC3621327  PMID: 23240599
ApoE4; diffusion tensor imaging (DTI); fractional anisotropy (FA); geriatrics; high angular resolution diffusion imaging; imaging genetics; structural brain networks
24.  Alzheimer’s Disease Risk Gene, GAB2, is Associated with Regional Brain Volume Differences in 755 Young Healthy Twins 
The development of late-onset Alzheimer’s disease (LOAD) is under strong genetic control and there is great interest in the genetic variants that confer increased risk. The Alzheimer’s disease risk gene, growth factor receptor bound protein 2-associated protein (GAB2), has been shown to provide a 1.27–1.51 increased odds of developing LOAD for rs7101429 major allele carriers, in case-control analysis. GAB2 is expressed across the brain throughout life, and its role in LOAD pathology is well understood. Recent studies have begun to examine the effect of genetic variation in the GAB2 gene on differences in the brain. However, the effect of GAB2 on the young-adult brain has yet to be considered. Here we found a significant association between the GAB2 gene and morphological brain differences in 755 young-adult twins (469 females) (M = 23.1, SD = 3.1 years), using a gene-based test with principal components regression (PCReg). Detectable differences in brain morphology are therefore associated with variation in the GAB2 gene, even in young adults, long before the typical age of onset of Alzheimer’s disease.
PMCID: PMC3785377  PMID: 22856364
GAB2; imaging genetics; tensor-based morphometry; Alzheimer’s disease
25.  Predicting White Matter Integrity from Multiple Common Genetic Variants 
Neuropsychopharmacology  2012;37(9):2012-2019.
Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate single-nucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20–30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained ∼6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.
PMCID: PMC3398730  PMID: 22510721
neuroimaging; brain structure; DTI; genetics; genetic profiles; prediction; imaging; clinical or preclinical; neuroanatomy; neurogenetics; pharmacogenetics / pharmacogenomics; neuroimaging; brain structure; DTI; genetics; genetic profiles

Results 1-25 (52)